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Databricks Data Engineer Professional Certification

Preparation Plan
Knowledge Base

Preparation Plan

Fundamentals & Databricks Tooling

Introduction to Databricks Platform and Delta Lake Fundamentals
Delta Lake Fundamentals and Transaction Log Concepts
ACID Properties in Delta Lake
Hands-on: Creating and Optimizing Delta Tables
Delta Lake Optimization Techniques
Z-Ordering and Advanced Indexing
Delta Clone Operations and Use Cases
Bloom Filters and File Size Optimization
Quiz: Databricks Tooling Fundamentals
Key Takeaways

Data Processing

Batch Processing Fundamentals
Spark Partitioning Strategies and Optimization
Partition Hints: Coalesce, Repartition, Rebalance
Hands-on: Optimizing Batch Workloads
Incremental Processing with Structured Streaming
Stream-Static Joins Implementation
Change Data Feed (CDF) and Incremental Data Processing
Quiz: Batch and Streaming Processing Techniques
Key Takeaways

Data Modeling & Security

Lakehouse Medallion Architecture
Data Quality Enforcement
Implementing Slowly Changing Dimensions (SCDs)
Hands-on: Building a Medallion Architecture Pipeline
Security & Governance Fundamentals
Dynamic Views for Data Masking
Row and Column-Level Security
Quiz: Data Modeling and Security Concepts
Key Takeaways

Monitoring, Testing & Deployment

Spark UI Performance Analysis
Monitoring Streaming Jobs and SLAs
Ganglia UI and Cluster UI Interpretation
Hands-on: Debugging Performance Issues
Databricks Jobs Configuration and Dependencies
Databricks CLI and REST API Implementation
CI/CD Patterns for Databricks
Quiz: Monitoring and Deployment Concepts
Key Takeaways

Knowledge Base

Fundamentals & Databricks Tooling

Introduction to Databricks Platform and Delta Lake Fundamentals

1. Introduction to Databricks Platform Architecture

The Databricks platform is built on a cloud-native architecture that separates control and data planes, providing both security and performance benefits.

Key Components:

The Databricks platform architecture consists of two primary planes:

Control Plane
- Managed by Databricks
- Handles authentication, authorization, job scheduling, and notebook orchestration
- Stores metadata about clusters, notebooks, and jobs
- Provides web UI for managing resources
Data Plane
- Resides in the customer’s cloud account (AWS, Azure, GCP)
- Contains the compute clusters that process data
- Stores data in cloud storage (S3, ADLS, GCS)
- Isolates customer data from Databricks infrastructure

Interaction Between Planes:

Control plane initiates and manages clusters in the data plane
Data remains within your cloud account boundaries
Authentication flows through the control plane to secure access

Key Exam Concepts:

Understanding the separation of responsibilities between planes
Security implications of this architecture
How resource provisioning works across the planes

2. Delta Lake Fundamentals and Transaction Log Concepts

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Core Delta Lake Components:

Parquet Files
- Base data storage format
- Columnar storage optimized for analytics
- Efficient compression and encoding
Transaction Log (_delta_log)
- JSON files that track all changes to the table
- Each transaction creates a new JSON file in the log
- Numbered sequentially (00000000000000000000.json, 00000000000000000001.json, etc.)
- Contains complete record of all operations (add, remove, update files)
Table State
- Current state of the table is derived by replaying the transaction log
- No need to scan all data files to understand table structure
- Enables time travel and versioned queries

Transaction Log Deep Dive:

The transaction log is the backbone of Delta Lake and enables many of its key features:

/table_path/
  ├── _delta_log/
  │   ├── 00000000000000000000.json
  │   ├── 00000000000000000001.json
  │   └── ...
  ├── part-00000-abc123.snappy.parquet
  ├── part-00001-def456.snappy.parquet
  └── ...

Each transaction log entry contains:

Operation type (write, commit, etc.)
Schema information
Partition information
Files added/removed
Operation metadata (user, timestamp, etc.)

Key Exam Concepts:

How Delta Lake uses transaction logs to provide ACID properties
How to interpret and use transaction log information
Understanding the relationship between Parquet files and log entries

3. ACID Properties in Delta Lake

Delta Lake is the first big data storage layer to provide full ACID (Atomicity, Consistency, Isolation, Durability) guarantees.

ACID Properties Explained:

Atomicity
- All changes in a transaction are applied completely or not at all
- Example: If a job fails during a write operation, no partial updates are visible
Consistency
- The table is always in a valid state
- Schema enforcement prevents inconsistent data types
- Constraints can be applied to ensure data quality
Isolation
- Delta Lake uses optimistic concurrency control
- Multiple readers can access the same table without interference
- Writers use versioning to prevent conflicts
Durability
- Once a transaction is committed, it’s permanent
- Cloud storage provides underlying durability
- Transaction log ensures recoverability

Optimistic Concurrency Control:

Delta Lake uses optimistic concurrency control (OCC) rather than locking:

Writers read the current table state from the transaction log
Changes are prepared based on that state
Before committing, the system checks if the table state has changed
If not, changes are committed; if yes, the operation retries or fails

Which transactions might conflict?

Concurrent schema changes
Concurrent writes to the same partition
Operations that modify the same files

Key Exam Concepts:

How Delta Lake implements each ACID property
Understanding optimistic concurrency control
Identifying which operations might conflict

4. Hands-on: Creating and Optimizing Delta Tables

Let’s walk through how to create and perform basic operations with Delta tables.

Creating a Delta Table:

# Creating a Delta table from a DataFrame
df = spark.read.csv("/path/to/data.csv", header=True, inferSchema=True)
df.write.format("delta").save("/path/to/delta-table")

# Creating a Delta table using SQL
spark.sql("""
CREATE TABLE my_delta_table
USING DELTA
LOCATION '/path/to/delta-table'
AS SELECT * FROM source_table
""")

Basic Operations:

# Reading a Delta table
df = spark.read.format("delta").load("/path/to/delta-table")

# Updating data
from delta.tables import DeltaTable

deltaTable = DeltaTable.forPath(spark, "/path/to/delta-table")
deltaTable.update(
  condition = "id = 100",
  set = { "value": "'new_value'" }
)

# Time travel query
df_previous_version = spark.read.format("delta").option("versionAsOf", 1).load("/path/to/delta-table")

Delta Table History:

# View table history
deltaTable.history().show()

Delta Table Details:

# Detailed information about the table
deltaTable.detail().show()

Key Exam Concepts:

Different ways to create Delta tables
Understanding basic operations (read, write, update, delete)
How to use time travel and history functions

5. Delta Lake Optimization Techniques

Delta Lake offers several optimization techniques to improve query performance and efficiency.

File Optimization: OPTIMIZE Command

The OPTIMIZE command compacts small files into larger ones:

-- Basic OPTIMIZE command
OPTIMIZE my_delta_table

-- OPTIMIZE with Z-ORDER
OPTIMIZE my_delta_table ZORDER BY (column1, column2)

Benefits:

Reduces the number of files that need to be processed
Improves scan efficiency
Decreases metadata overhead

Partitioning Delta Tables:

-- Creating a partitioned table
CREATE TABLE sales_data
USING DELTA
PARTITIONED BY (date)
LOCATION '/path/to/sales_data'
AS SELECT * FROM source_data

-- Writing to a partitioned table from DataFrame
df.write.format("delta")
  .partitionBy("date")
  .save("/path/to/sales_data")

Partitioning Best Practices:

Choose columns with high cardinality but not too high (hundreds to thousands of unique values)
Consider query patterns (filter columns are good partition candidates)
Avoid over-partitioning, which creates too many small files
Typically use date/time dimensions, region, or category columns

Key Exam Concepts:

When and how to partition data
Trade-offs in partition design decisions
How to optimize existing tables

6. Z-Ordering and Advanced Indexing

Z-ordering is a multi-dimensional clustering technique that helps co-locate related data.

Z-Ordering Example:

-- Apply Z-ORDER to specific columns
OPTIMIZE my_delta_table ZORDER BY (customer_id, product_id)

How Z-Ordering Works:

Groups related records together based on column values
Enables data skipping during queries
Particularly effective for high-cardinality columns

Z-Order vs. Partitioning:

Partitioning: Physical separation of data
Z-ordering: Logical organization within files
Partitioning works at file level; Z-ordering works within files
Use both together for optimal performance

Data Skipping:

Delta Lake automatically collects statistics on data files:

Min/max values for columns
NULL counts
Approximate count distinct

These statistics enable the query optimizer to skip files that can’t contain matching data.

Key Exam Concepts:

When to use Z-ordering vs. partitioning
How to choose columns for Z-ordering
Understanding data skipping mechanisms

7. Delta Clone Operations and Use Cases

Delta clone creates a copy of a Delta table with minimal data duplication.

Types of Clones:

Shallow Clone
```
CREATE TABLE clone_table
SHALLOW CLONE source_table
```
- Creates a reference to source table files
- Independent transaction logs
- Changes to either table don’t affect the other
- Very fast, minimal storage overhead
Deep Clone
```
CREATE TABLE clone_table
DEEP CLONE source_table
```
- Copies both data files and transaction log
- Completely independent from source
- Higher storage overhead but full isolation

Common Use Cases:

Testing data modifications without affecting source data
Creating development/test environments
Sharing data snapshots with other teams
Backup and disaster recovery strategies

Key Exam Concepts:

Differences between shallow and deep clones
When to use each type of clone
How clones interact with the source table

8. Bloom Filters and File Size Optimization

Bloom Filters:

Bloom filters are a space-efficient probabilistic data structure used to test whether an element is a member of a set.

-- Create a bloom filter index
CREATE BLOOMFILTER INDEX
ON TABLE my_delta_table
FOR COLUMNS(customer_id OPTIONS (fpp=0.1, numItems=10000000))

Benefits:

Faster point lookups
Reduced I/O for selective queries
Complement to Z-ordering for high-cardinality columns

File Size Optimization:

Optimizing file sizes is crucial for Spark performance:

Target file size: 128MB - 1GB
Too small: High metadata overhead
Too large: Inefficient parallelism

Controlling File Size:

# Using repartition to control number of files
df.repartition(8).write.format("delta").save("/path/to/table")

# Using coalesce for fewer files
df.coalesce(4).write.format("delta").save("/path/to/table")

Auto Optimize:

-- Enable auto optimization for a table
ALTER TABLE my_delta_table SET TBLPROPERTIES(
  'delta.autoOptimize.optimizeWrite' = 'true',
  'delta.autoOptimize.autoCompact' = 'true'
)

Key Exam Concepts:

When to use Bloom filters
How to configure optimal file sizes
Trade-offs in file size decisions
Auto optimization settings

Quiz: Databricks Tooling Fundamentals

Let’s test your understanding of today’s material with a short quiz:

What happens if two users try to update the same Delta table simultaneously? a) The first operation succeeds, and the second fails with a conflict error b) Both operations succeed, with the second overwriting the first c) Both operations fail with a locking error d) Delta automatically merges both changes
Which Delta Lake feature allows you to query the table as it appeared at a specific point in time? a) Time series b) Time travel c) Snapshot isolation d) Version control
What is the primary purpose of the Delta transaction log? a) To store table schemas b) To record all changes to the table c) To optimize query performance d) To enforce security policies
Which optimization technique is better suited for high-cardinality columns? a) Partitioning b) Z-ordering c) Bloom filters d) B-tree indexing
What is the difference between a shallow clone and a deep clone? a) Shallow clone copies only the schema, deep clone copies schema and data b) Shallow clone references original data files, deep clone copies data files c) Shallow clone is read-only, deep clone is read-write d) There is no difference, they’re synonyms
Which command would you use to compact small files in a Delta table? a) COMPACT b) OPTIMIZE c) VACUUM d) MERGE
What is the purpose of data skipping in Delta Lake? a) To avoid processing deleted records b) To skip unnecessary files during queries c) To ignore NULL values during aggregations d) To bypass schema validation
In the Databricks architecture, where does customer data reside? a) In the Databricks control plane b) In the customer’s data plane within their cloud account c) Across both control and data planes d) In a shared multi-tenant storage system

Answers and Explanations:

a) Delta Lake uses optimistic concurrency control. The first operation succeeds, and the second will detect the conflict and fail.
b) Time travel allows you to query the table as it appeared at a specific version or timestamp.
b) The transaction log records all changes (adds, removes, updates) to the table, enabling ACID properties.
b) Z-ordering is better for high-cardinality columns, while partitioning works better with lower-cardinality columns.
b) A shallow clone references the original data files but has its own transaction log, while a deep clone creates copies of both data and transaction log.
b) OPTIMIZE is used to compact small files into larger ones for better performance.
b) Data skipping uses file-level statistics to avoid processing files that can’t match query predicates.
b) In the Databricks architecture, customer data resides in the data plane within the customer’s own cloud account.

Key Takeaways

We covered the foundational elements of Databricks and Delta Lake:

Databricks Architecture: Understanding the separation between control and data planes
Delta Lake Fundamentals: How the transaction log and Parquet files work together
ACID Properties: How Delta Lake implements atomicity, consistency, isolation, and durability
Delta Table Operations: Creating and working with Delta tables
Optimization Techniques: Partitioning, Z-ordering, and file size management
Advanced Features: Cloning, Bloom filters, and data skipping

Data Processing

1. Batch Processing Fundamentals

Batch processing is the foundation of data engineering in Databricks, enabling the processing of large volumes of data in defined intervals.

Core Batch Processing Concepts:

Batch vs. Streaming:

Batch processing works with bounded, complete datasets
Processing occurs at scheduled intervals or trigger events
Results are typically written to persistent storage

Apache Spark Execution Model:

Understanding Spark’s execution model is crucial for optimizing batch jobs:

Logical Plan: DataFrame operations create a logical plan
Physical Plan: Catalyst optimizer converts logical plan to physical execution plan
Tasks: Physical plan is divided into stages and tasks
Execution: Tasks are distributed across the cluster

Transformations and Actions:

Transformations: Operations that create a new DataFrame (lazy evaluation)
- Examples: select(), filter(), groupBy()
Actions: Operations that trigger computation and return results
- Examples: count(), collect(), write()

Job → Stage → Task Hierarchy:

Job: Triggered by an action
Stage: Created when data needs to be shuffled
Task: Individual unit of work on a partition

Key Performance Metrics:

Job Duration: Total time to complete all operations
Stage Duration: Time to complete a specific stage
Task Duration: Time for individual tasks (look for skew)
Shuffle Read/Write: Amount of data transferred between executors

Key Exam Concepts:

Understanding Spark’s execution model
Differentiating between transformations and actions
Interpreting performance metrics

2. Spark Partitioning Strategies and Optimization

Effective partitioning is critical for Spark performance optimization.

Partitioning Fundamentals:

Partition: Logical chunk of data processed by one task
Parallelism: Number of partitions determines degree of parallelism
Data Skew: Uneven distribution of data across partitions

Partitioning Strategies:

Input Partitioning:
- How data is initially divided when read
- Affected by file format, size, and storage system
Processing Partitioning:
- How data is organized during computation
- Controlled by shuffle operations
Output Partitioning:
- How data is organized when written
- Affects future query performance

Ideal Partition Size:

Target: 128MB - 1GB per partition
Too small: High scheduling overhead
Too large: Inefficient memory usage, potential OOM errors

Handling Data Skew:

Data skew occurs when certain partitions contain significantly more data than others:

Identify Skew:
- Examine task durations in Spark UI
- Look for outliers in task metrics
Mitigate Skew:
- Salting/key normalization for skewed join keys
- Custom partitioning for known skewed columns
- Broadcasting small datasets for joins

Key Exam Concepts:

How to determine optimal partition counts
Techniques for identifying and resolving data skew
Impact of partitioning on shuffle operations

3. Partition Hints: Coalesce, Repartition, Rebalance

Spark provides several mechanisms to control partitioning during processing.

Repartition vs. Coalesce:

Repartition:
```
# Repartition to 200 partitions
df = df.repartition(200)
   
# Repartition by specific column(s)
df = df.repartition(200, "customer_id")
```
- Full shuffle (expensive)
- Can increase or decrease partition count
- Distributes data evenly
- Use when need to increase partitions or balance data
Coalesce:
```
# Reduce to 50 partitions
df = df.coalesce(50)
```
- Avoids full shuffle when reducing partitions
- Can only reduce partition count, not increase
- May result in uneven partitions
- More efficient than repartition for reducing partitions

Repartition by Range:

# Repartition by range of values
df = df.repartitionByRange(200, "timestamp")

Ensures similar ranges of values go to the same partition
Useful for time-series data
Creates more evenly distributed partitions for ordered data

Rebalance:

# Available in Databricks Runtime (not in open-source Spark)
df = df.rebalance()

Adaptive redistribution of data
Less expensive than full repartition
Used to correct skew without specifying partition count

When to Use Each Approach:

Operation	When to Use	Cost	Effect on Skew
Repartition	Need specific partition count, even distribution	High	Resolves skew
Coalesce	Need to reduce partitions efficiently	Low	May worsen skew
RepartitionByRange	Working with ordered data	High	Good for ordered data
Rebalance	Need to correct skew adaptively	Medium	Resolves moderate skew

Key Exam Concepts:

Appropriate usage of each partitioning operation
Performance implications of different partition operations
How to optimize partition operations

4. Hands-on: Optimizing Batch Workloads

Let’s walk through practical techniques for optimizing batch processing workloads.

Scenario: Processing Customer Transaction Data

Consider a large dataset of customer transactions that needs to be processed daily:

# Read raw transaction data
transactions = spark.read.format("delta").load("/data/raw/transactions")

# Initial processing
processed = transactions.filter(transactions.status == "completed") \
                         .join(customers, "customer_id") \
                         .groupBy("customer_id", "date") \
                         .agg(sum("amount").alias("daily_total"))

# Write results
processed.write.format("delta").mode("overwrite").save("/data/processed/daily_totals")

Step 1: Analyze the Current Job

Use the Spark UI to identify bottlenecks:

Examine stage durations
Look for skewed tasks
Check shuffle read/write metrics

Step 2: Optimize Input

# Add partition pruning
transactions = spark.read.format("delta") \
                     .load("/data/raw/transactions") \
                     .filter(transactions.date == current_date)

# Broadcast small tables
customers = spark.read.format("delta").load("/data/customers")
broadcast_customers = F.broadcast(customers)

Step 3: Optimize Processing

# Add appropriate partitioning
processed = transactions.filter(transactions.status == "completed") \
                         .join(broadcast_customers, "customer_id") \
                         .repartition(200, "customer_id") \
                         .groupBy("customer_id", "date") \
                         .agg(sum("amount").alias("daily_total"))

Step 4: Optimize Output

# Optimize output partitioning
processed.repartition(100) \
         .write.format("delta") \
         .partitionBy("date") \
         .mode("overwrite") \
         .option("dataChange", "false") \
         .save("/data/processed/daily_totals")

Step 5: Implement Caching for Iterative Processing

# Cache intermediate results when reused
enriched_transactions = transactions.filter(transactions.status == "completed") \
                                   .join(broadcast_customers, "customer_id")
enriched_transactions.cache()

# Use for multiple aggregations
daily_totals = enriched_transactions.groupBy("customer_id", "date") \
                                    .agg(sum("amount").alias("daily_total"))

product_analysis = enriched_transactions.groupBy("product_id") \
                                        .count()

# Unpersist when done
enriched_transactions.unpersist()

Key Optimization Techniques:

Partition Pruning: Filter by partition columns early
Join Optimization: Broadcast small tables
Appropriate Partitioning: Match partitions to cluster size
Caching: Cache reused DataFrames
Adaptive Query Execution: Enable for automatic optimizations
Predicate Pushdown: Push filters to data source
AQE Skew Join: Enable adaptive handling of skewed joins

Key Exam Concepts:

Identifying and addressing performance bottlenecks
Implementing appropriate optimization techniques
Understanding when to use different optimization strategies

5. Incremental Processing with Structured Streaming

Structured Streaming brings real-time data processing capabilities to Spark’s DataFrame API.

Structured Streaming Fundamentals:

Core Concepts:

Unbounded Table: Stream is treated as a continuously growing table
Trigger: Controls when to process new data
Output Modes: How results are updated
Watermarks: Handling late data
Checkpointing: Maintaining state between batches

Stream Sources:

# Reading from a stream source
stream_df = spark.readStream.format("delta") \
                           .option("maxFilesPerTrigger", 10) \
                           .load("/data/source")

Common stream sources:

Delta Lake tables
Kafka
Files in a directory (Auto Loader)
Event Hubs (Azure)

Processing Modes:

Micro-Batch Processing:
- Default mode
- Processes data in small batches
- Lower latency than traditional batch, higher throughput than continuous
Continuous Processing:
- Sub-millisecond latency
- Limited operation support
- Lower throughput than micro-batch

Output Modes:

Append Mode:
```
# Only new rows are written to sink
query = df.writeStream.outputMode("append")...
```
- Only new results are sent to sink
- Cannot update previous results
- Works with all queries
Complete Mode:
```
# Full result table is written each time
query = df.writeStream.outputMode("complete")...
```
- Entire result table is written each time
- Requires aggregation
- Higher resource utilization
Update Mode:
```
# Only updated rows are written
query = df.writeStream.outputMode("update")...
```
- Only rows that changed are written
- Combines benefits of append and complete
- Not supported by all sinks

Watermarking:

# Setting a watermark for handling late data
df = df.withWatermark("event_time", "10 minutes") \
       .groupBy(window("event_time", "5 minutes"), "device") \
       .count()

Defines how late data can arrive
Enables state cleanup for aggregations
Essential for managing memory usage in long-running streams

Key Exam Concepts:

Understanding streaming concepts and terminology
Selecting appropriate processing modes and output modes
Implementing watermarks for late data handling

6. Stream-Static Joins Implementation

Joining streaming data with static (batch) data is a common pattern in data engineering.

Types of Stream Joins:

Stream-Static Join:

# Static DataFrame
static_df = spark.read.format("delta").load("/data/static")
   
# Streaming DataFrame
stream_df = spark.readStream.format("delta").load("/data/stream")
   
# Join
joined_df = stream_df.join(static_df, "join_key")

Stream joined with static DataFrame
Static data can be broadcast for efficiency
Static data is loaded once per trigger

Stream-Stream Join:

# Two streaming sources
stream1 = spark.readStream.format("delta").load("/data/stream1")
stream2 = spark.readStream.format("delta").load("/data/stream2")
   
# Join with watermarks
joined = stream1.withWatermark("time1", "10 minutes") \
                .join(
                  stream2.withWatermark("time2", "5 minutes"),
                  expr("join_key AND time1 >= time2 - interval 5 minutes AND time1 <= time2 + interval 5 minutes"),
                  "inner"
                )

Joins two streams
Requires watermarks and join conditions on event time
More complex state management

State Management:

Stream joins maintain state information between batches:

State size depends on watermark configuration
Improper watermarks can lead to memory issues
Checkpoint location stores state between restarts

Optimizing Stream-Static Joins:

Broadcast Join:

# Broadcast the static DataFrame
joined_df = stream_df.join(F.broadcast(static_df), "join_key")

Efficient for small static tables
Reduces shuffle operations

State Store Optimization:

# Configure state store
spark.conf.set("spark.sql.streaming.stateStore.providerClass", 
              "org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider")

RocksDB state store for large state
More efficient than default in-memory store

Watermark Tuning:

# Tight watermark for state cleanup
df = df.withWatermark("event_time", "30 minutes")

Balance between late data and state size

Key Exam Concepts:

Implementing and optimizing stream-static joins
Managing state in streaming applications
Tuning for performance and reliability

7. Change Data Feed (CDF) and Incremental Data Processing

Change Data Feed (CDF) captures row-level changes in Delta tables, enabling incremental processing patterns.

Change Data Feed Overview:

-- Enable CDF for a table
ALTER TABLE my_table SET TBLPROPERTIES (delta.enableChangeDataFeed = true)

-- Create a new table with CDF enabled
CREATE TABLE my_table (id INT, name STRING)
TBLPROPERTIES (delta.enableChangeDataFeed = true)

Tracks insert, update, and delete operations
Maintains versioned history of changes
Enables efficient incremental processing

Reading Change Data:

# Read changes since version 10
changes = spark.read.format("delta") \
               .option("readChangeData", "true") \
               .option("startingVersion", 10) \
               .table("my_table")

Each CDF record contains:

The modified data
Operation type (insert, update, delete)
Commit version and timestamp

CDC vs. CDF:

CDC (Change Data Capture): Source system feature that captures changes
CDF (Change Data Feed): Delta Lake feature that exposes changes

APPLY CHANGES INTO:

The APPLY CHANGES INTO operation simplifies CDC/CDF processing:

-- Apply changes from source to target
APPLY CHANGES INTO target_table
FROM source_cdc_table
KEYS (id)
SEQUENCE BY sequence_num

Automatically handles inserts, updates, deletes
Simplifies complex merge operations
Requires unique key and sequence columns

Implementing CDC Pipeline:

# Read CDC data
cdc_df = spark.readStream.format("delta") \
                         .option("readChangeData", "true") \
                         .load("/data/source")

# Process by operation type
def process_cdc_batch(batch_df, batch_id):
    # Extract operation types
    inserts = batch_df.filter("_change_type = 'insert'").drop("_change_type")
    updates = batch_df.filter("_change_type = 'update'").drop("_change_type")
    deletes = batch_df.filter("_change_type = 'delete'").drop("_change_type")
    
    # Apply to target table
    if not inserts.isEmpty():
        inserts.write.format("delta").mode("append").save("/data/target")
    
    if not updates.isEmpty() or not deletes.isEmpty():
        deltaTable = DeltaTable.forPath(spark, "/data/target")
        
        # Handle updates
        if not updates.isEmpty():
            deltaTable.alias("target").merge(
                updates.alias("updates"),
                "target.id = updates.id"
            ).whenMatched().updateAll().execute()
        
        # Handle deletes
        if not deletes.isEmpty():
            deltaTable.alias("target").merge(
                deletes.alias("deletes"),
                "target.id = deletes.id"
            ).whenMatched().delete().execute()

# Apply foreachBatch function
query = cdc_df.writeStream \
             .foreachBatch(process_cdc_batch) \
             .option("checkpointLocation", "/checkpoints/cdc") \
             .start()

Key Exam Concepts:

Enabling and using Change Data Feed
Implementing incremental processing with CDF
Understanding CDF vs. CDC concepts

8. Quiz: Batch and Streaming Processing Techniques

Let’s test your understanding of today’s material with a short quiz:

Which of the following operations will trigger a shuffle in Spark? a) filter() b) select() c) groupBy() d) withColumn()
What is the primary difference between repartition() and coalesce()? a) repartition() can only increase partitions, coalesce() can only decrease them b) repartition() performs a full shuffle, coalesce() minimizes shuffling c) repartition() works on RDDs, coalesce() works on DataFrames d) repartition() is faster but uses more memory
In Structured Streaming, which output mode requires an aggregation operation? a) Append b) Update c) Complete d) Incremental
What is the purpose of watermarking in Structured Streaming? a) To filter out records with timestamps older than a threshold b) To handle late-arriving data and manage state cleanup c) To ensure exactly-once processing semantics d) To synchronize processing across multiple streams
Which join strategy is most efficient when joining a streaming DataFrame with a small static DataFrame? a) Shuffle join b) Broadcast join c) Sort-merge join d) Nested loop join
What does Change Data Feed (CDF) track in Delta Lake? a) Schema changes only b) Row-level changes (inserts, updates, deletes) c) Partition changes only d) Metadata changes only
Which operation is used to efficiently propagate changes from a source CDC table to a target Delta table? a) INSERT OVERWRITE b) MERGE INTO c) APPLY CHANGES INTO d) UPDATE SET
What is the main advantage of using foreachBatch in Structured Streaming over standard output sinks? a) It provides higher throughput b) It allows you to apply different operations to each batch c) It guarantees exactly-once processing d) It eliminates the need for checkpointing

Answers and Explanations:

c) groupBy() - This operation requires data with the same keys to be on the same partition, which necessitates a shuffle operation.
b) repartition() performs a full shuffle, coalesce() minimizes shuffling - repartition() redistributes data evenly through a full shuffle, while coalesce() combines existing partitions with minimal data movement.
c) Complete - Complete output mode writes the entire result table each time, which requires an aggregation to maintain the complete state.
b) To handle late-arriving data and manage state cleanup - Watermarking defines how late data can arrive and allows the system to clean up state for records older than the watermark.
b) Broadcast join - Broadcasting a small static DataFrame to all executors eliminates the need for shuffling in the streaming job.
b) Row-level changes (inserts, updates, deletes) - CDF tracks all row-level modifications to the table, including the operation type and the changed data.
c) APPLY CHANGES INTO - This operation is specifically designed to handle CDC data, automatically applying inserts, updates, and deletes based on the source data.
b) It allows you to apply different operations to each batch - foreachBatch gives you complete control over how each micro-batch is processed, enabling complex logic that isn’t possible with standard sinks.

Key Takeaways

we covered essential data processing techniques in Databricks:

Batch Processing Fundamentals: Understanding Spark’s execution model and optimizing batch jobs
Partitioning Strategies: Techniques for distributing data effectively across a cluster
Partition Operations: Using coalesce, repartition, and rebalance to control data distribution
Batch Optimization: Practical techniques for improving batch processing performance
Structured Streaming: Processing real-time data with Spark’s streaming capabilities
Stream-Static Joins: Combining streaming and static data effectively
Change Data Feed: Capturing and processing incremental changes in Delta tables

Data Modeling & Security

1. Lakehouse Medallion Architecture

The medallion architecture is a design pattern that organizes data in the Lakehouse into three distinct layers, each representing a different level of data refinement.

Core Layers of the Medallion Architecture:

Bronze Layer (Raw)
- Raw data ingested from source systems
- Preserves original data format and content
- Minimal or no transformations
- Serves as historical archive
- Example: JSON files from API, CSV exports, database dumps
Silver Layer (Cleansed)
- Validated and cleansed data
- Standardized schema and data types
- Duplicate records removed
- Basic quality rules applied
- Typically in Delta format with optimizations
- Example: Parsed JSON, validated timestamps, removed duplicates
Gold Layer (Business)
- Aggregated and enriched for specific use cases
- Optimized for query performance
- Business metrics and KPIs
- Often dimensional models or wide tables
- Serves as source for reporting and ML
- Example: Customer 360 view, sales aggregations by region

Data Flow Through the Medallion Architecture:

Source Systems → Bronze → Silver → Gold → Consumption

Implementation Considerations:

Schema Evolution: Bronze preserves all fields for future use
Processing Patterns: Batch vs. streaming at different layers
Retention Policies: Different for each layer (longer for bronze)
Access Patterns: More restricted for bronze, broader for gold
Optimization: Increases from bronze to gold

Key Exam Concepts:

Purpose and characteristics of each layer
How data flows through the architecture
Implementation strategies for each layer

2. Data Quality Enforcement

Data quality is essential for reliable analytics and decision-making. Delta Lake provides several mechanisms for enforcing data quality.

Data Quality Enforcement Techniques:

Schema Enforcement:

Built into Delta Lake
Ensures data conforms to expected structure
Prevents schema drift

# Schema enforcement in action
from pyspark.sql.types import StructType, StructField, StringType, IntegerType

schema = StructType([
    StructField("id", StringType(), False),
    StructField("name", StringType(), True),
    StructField("age", IntegerType(), True)
])

# Write with schema
df.write.format("delta").schema(schema).save("/path/to/table")

# Attempt to write incompatible data will fail
incompatible_df.write.format("delta").mode("append").save("/path/to/table")

Constraints:

Business rules applied to data
Enforced during write operations

-- Add NOT NULL constraint
ALTER TABLE customer_data ADD CONSTRAINT valid_id CHECK (id IS NOT NULL);

-- Add uniqueness constraint
ALTER TABLE customer_data ADD CONSTRAINT unique_id CHECK (id IS NOT NULL AND id <> '');

-- Add value range constraint
ALTER TABLE customer_data ADD CONSTRAINT valid_age CHECK (age > 0 AND age < 120);

Quality Expectations in Delta Live Tables:

# Bronze layer with minimal expectations
@dlt.table
def bronze_customer_data():
    return spark.readStream.format("cloudFiles") \
        .option("cloudFiles.format", "json") \
        .load("/data/raw/customers")

# Silver layer with quality constraints
@dlt.table(
    constraint="valid_customer_record",
    expect_or_drop="id IS NOT NULL AND age > 0"
)
def silver_customer_data():
    return dlt.read("bronze_customer_data") \
        .dropDuplicates(["id"]) \
        .withColumn("processed_timestamp", current_timestamp())

Enforcement Modes:

# Fail the entire batch if any record violates constraints
@dlt.table(
    constraint="valid_metric",
    expect_or_fail="metric_value > 0"
)
   
# Drop records that violate constraints
@dlt.table(
    constraint="valid_customer",
    expect_or_drop="id IS NOT NULL AND age > 0"
)
   
# Quarantine records that violate constraints
@dlt.table
def silver_good_records():
    return dlt.read("bronze_table").where("id IS NOT NULL")
   
@dlt.table
def silver_bad_records():
    return dlt.read("bronze_table").where("id IS NULL")

Implementing a Data Quality Framework:

Define Quality Dimensions:
- Completeness: Are all expected values present?
- Accuracy: Are values correct?
- Consistency: Are related values consistent?
- Uniqueness: Are unique values truly unique?
- Timeliness: Is data current?
Implement Testing Layers:
- Bronze: Format validation, completeness checks
- Silver: Business rule validation, referential integrity
- Gold: Aggregation consistency, trend validation
Monitor Quality Metrics:
- Track failure rates over time
- Create quality dashboards
- Implement alerts for quality degradation

Key Exam Concepts:

Different techniques for enforcing data quality
How to implement quality rules at each layer
Handling quality violations appropriately

3. Implementing Slowly Changing Dimensions (SCDs)

Slowly Changing Dimensions (SCDs) are techniques for handling historical changes in dimensional data. Delta Lake provides powerful capabilities for implementing various SCD types.

SCD Type 0: Retain Original

Dimension attributes never change
Historical accuracy is not maintained
Example: Birth date, original customer acquisition date

# Simple overwrite with only new records (no updates to existing)
new_customers.write.format("delta").mode("append").saveAsTable("customers_type0")

SCD Type 1: Overwrite

Replaces old values with new values
No history is maintained
Example: Correcting errors, updating phone numbers

# Using MERGE to implement Type 1 SCD
from delta.tables import DeltaTable

deltaTable = DeltaTable.forName(spark, "customers_type1")

# Merge operation
deltaTable.alias("target").merge(
    source=updates_df.alias("updates"),
    condition="target.customer_id = updates.customer_id"
).whenMatched().updateAll().execute()

SCD Type 2: Add New Row

Maintains complete history
Creates new records for changes
Tracks current vs. historical records
Example: Customer address changes, organizational changes

# Read current dimension table
dim_customer = spark.read.format("delta").table("dim_customer")

# Identify changed records
# 1. Join new data with current dimension
# 2. Filter for changes in tracked columns
changes = new_data.alias("new") \
    .join(dim_customer.alias("current"), 
          "customer_id", 
          "left_outer") \
    .where("current.customer_id IS NULL OR " + 
           "new.name <> current.name OR " + 
           "new.address <> current.address") \
    .select("new.*")

# Expire current records
if not changes.isEmpty():
    deltaTable = DeltaTable.forName(spark, "dim_customer")
    
    # Match records to be updated
    matched_records = deltaTable.alias("dim") \
        .merge(
            changes.alias("updates"),
            "dim.customer_id = updates.customer_id AND dim.is_current = true"
        )
    
    # Update matched records (expire them)
    matched_records.whenMatched().updateExpr({
        "is_current": "false",
        "end_date": "current_date()",
        "updated_timestamp": "current_timestamp()"
    })
    
    # Insert new versions
    .whenNotMatched().insertExpr({
        "customer_id": "updates.customer_id",
        "name": "updates.name",
        "address": "updates.address",
        "is_current": "true",
        "start_date": "current_date()",
        "end_date": "null",
        "updated_timestamp": "current_timestamp()"
    }).execute()

SCD Type 3: Add New Columns

Maintains limited history (typically current and previous)
Adds columns for previous values
Example: Previous address, former name

# Read current dimension
dim_product = spark.read.format("delta").table("dim_product")

# Identify changes
changes = new_data.alias("new") \
    .join(dim_product.alias("current"), 
          "product_id", 
          "inner") \
    .where("new.category <> current.category") \
    .select("new.*", "current.category AS previous_category")

# Apply Type 3 updates
if not changes.isEmpty():
    deltaTable = DeltaTable.forName(spark, "dim_product")
    
    deltaTable.alias("dim").merge(
        changes.alias("updates"),
        "dim.product_id = updates.product_id"
    ).whenMatched().updateExpr({
        "previous_category": "dim.category",
        "category": "updates.category",
        "updated_timestamp": "current_timestamp()"
    }).execute()

SCD Type 6: Hybrid Approach (Combined Type 1 + 2 + 3)

Maintains current and historical records (Type 2)
Updates current version attributes for Type 1 fields
Maintains versioning for Type 2 fields
Example: Customer dimension with both fixed and tracked attributes

# Complex implementation that combines Type 1, 2, and 3 approaches
# Not showing full code due to complexity, but key concepts include:
# 1. Identifying which attributes are Type 1 vs. Type 2
# 2. Maintaining version numbers for records
# 3. Using both effective dates and is_current flags
# 4. Selective updating based on attribute type

Key Exam Concepts:

Understanding different SCD types and their use cases
Implementing SCDs using Delta Lake operations
Managing current vs. historical records

4. Hands-on: Building a Medallion Architecture Pipeline

Let’s walk through implementing a complete medallion architecture pipeline using Delta Lake and Delta Live Tables.

Scenario: Customer Order Processing Pipeline

We’ll build a pipeline that processes customer order data through the bronze, silver, and gold layers.

1. Bronze Layer: Raw Data Ingestion

# Using Delta Live Tables
@dlt.table(
    name="bronze_orders",
    comment="Raw order data from JSON files"
)
def bronze_orders():
    return (
        spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "json")
        .option("cloudFiles.schemaLocation", "/tmp/schema/orders")
        .load("/data/raw/orders")
    )

@dlt.table(
    name="bronze_customers",
    comment="Raw customer data from CSV files"
)
def bronze_customers():
    return (
        spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "csv")
        .option("cloudFiles.schemaLocation", "/tmp/schema/customers")
        .option("header", "true")
        .load("/data/raw/customers")
    )

2. Silver Layer: Cleansed and Validated Data

@dlt.table(
    name="silver_customers",
    comment="Cleansed customer records",
    constraint="valid_customer_id",
    expect_or_drop="customer_id IS NOT NULL"
)
def silver_customers():
    return (
        dlt.read("bronze_customers")
        .dropDuplicates(["customer_id"])
        .withColumn("processed_date", current_date())
        .withColumn("valid_email", 
                   F.expr("email RLIKE '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,6}$'"))
    )

@dlt.table(
    name="silver_orders",
    comment="Cleansed order records with validated references",
    constraint="valid_order", 
    expect_or_drop="order_id IS NOT NULL AND customer_id IS NOT NULL AND amount > 0"
)
def silver_orders():
    # Join with customers to validate references
    customers = dlt.read("silver_customers").select("customer_id").distinct()
    
    return (
        dlt.read("bronze_orders")
        .dropDuplicates(["order_id"])
        .withColumn("order_date", F.to_date(F.col("order_timestamp")))
        .withColumn("processed_date", current_date())
        .join(customers, "customer_id", "left_semi")  # Only keep orders with valid customers
    )

3. Gold Layer: Business-Ready Aggregations

@dlt.table(
    name="gold_customer_orders",
    comment="Customer order summaries by day"
)
def gold_customer_orders():
    return (
        dlt.read("silver_orders")
        .groupBy("customer_id", "order_date")
        .agg(
            F.count("order_id").alias("order_count"),
            F.sum("amount").alias("total_amount"),
            F.avg("amount").alias("avg_order_value"),
            F.max("amount").alias("largest_order")
        )
    )

@dlt.table(
    name="gold_daily_sales",
    comment="Daily sales aggregations"
)
def gold_daily_sales():
    return (
        dlt.read("silver_orders")
        .groupBy("order_date")
        .agg(
            F.count("order_id").alias("total_orders"),
            F.sum("amount").alias("total_sales"),
            F.countDistinct("customer_id").alias("unique_customers")
        )
    )

4. Pipeline Configuration

{
  "name": "orders_pipeline",
  "configuration": {
    "pipelines.autoOptimize.managed": "true",
    "pipelines.autoOptimize.zOrderCols": {
      "silver_orders": ["order_date", "customer_id"],
      "silver_customers": ["customer_id"],
      "gold_customer_orders": ["order_date", "customer_id"],
      "gold_daily_sales": ["order_date"]
    }
  },
  "clusters": [
    {
      "label": "default",
      "autoscale": {
        "min_workers": 1,
        "max_workers": 5
      }
    }
  ],
  "continuous": false,
  "development": false
}

Key Implementation Concepts:

Validation and quality checks increase from bronze to gold
Each layer builds on and refines the previous layer
Performance optimizations are targeted based on query patterns
Business logic is encapsulated in the transformations

Key Exam Concepts:

Designing appropriate transformations for each layer
Implementing data quality checks at each stage
Optimizing for downstream consumption patterns

5. Security & Governance Fundamentals

Security and governance are critical aspects of enterprise data platforms, ensuring data protection, compliance, and appropriate access controls.

Core Security & Governance Areas:

Data Security
- Encryption (at-rest and in-transit)
- Access control and authentication
- Secure communication
Data Governance
- Metadata management
- Data lineage and cataloging
- Data quality management
Compliance
- Regulatory requirements (GDPR, CCPA, HIPAA, etc.)
- Audit logging and monitoring
- Data retention policies
Privacy
- Data masking and anonymization
- Right to be forgotten
- Consent management

Databricks Security Architecture:

Databricks implements security at multiple layers:

Network Security
- VPC/VNET integration
- Private link endpoints
- IP access lists
Identity Management
- SCIM provisioning
- SSO integration
- Service principals
Data Access Control
- Table ACLs
- Column-level security
- Row-level filtering
Audit & Compliance
- Comprehensive audit logs
- Workspace access logs
- Query history

Databricks Unity Catalog:

Unity Catalog provides a unified governance solution for the Databricks Lakehouse:

Three-Level Namespace
- Catalog
- Schema/Database
- Table/View/Function
Securables
- Data objects (tables, views, etc.)
- Compute resources (warehouses, clusters)
- Code assets (notebooks, jobs)
Permissions Model
- Identity-based access control
- Fine-grained permissions
- Inheritance hierarchy

Key Exam Concepts:

Four pillars of data governance
Databricks security implementation
Unity Catalog structure and benefits

6. Dynamic Views for Data Masking

Dynamic views provide a powerful mechanism for implementing data masking and access control in Databricks.

Dynamic Views Concept:

SQL views with custom logic based on user attributes
Enable row and column-level security
Create different “views” of the same data for different users
Implement data masking for sensitive fields

Creating a Dynamic View for Column Masking:

-- Create a view that masks PII based on user group
CREATE OR REPLACE VIEW masked_customer_data AS
SELECT
  customer_id,
  -- Full name only visible to admins
  CASE WHEN is_member('admins') THEN full_name
       ELSE 'REDACTED'
  END AS full_name,
  -- Last 4 digits of SSN for finance team, nothing for others
  CASE WHEN is_member('admins') THEN ssn
       WHEN is_member('finance') THEN CONCAT('XXX-XX-', RIGHT(ssn, 4))
       ELSE 'XXX-XX-XXXX'
  END AS ssn,
  -- Email domain only for most users
  CASE WHEN is_member('admins') THEN email
       WHEN is_member('marketing') THEN email
       ELSE CONCAT('user@', SPLIT(email, '@')[1])
  END AS email,
  -- Only show birth year for most users
  CASE WHEN is_member('admins') THEN date_of_birth
       ELSE CONCAT(YEAR(date_of_birth), '-01-01')
  END AS date_of_birth,
  -- Non-sensitive fields visible to all
  address_city,
  address_state,
  customer_status
FROM customers

Row-Level Security with Dynamic Views:

-- Create a view with row-level filtering based on region access
CREATE OR REPLACE VIEW regional_sales AS
SELECT *
FROM sales
WHERE
  -- Admins see all regions
  is_member('admins') OR
  -- Regional managers see only their region
  (is_member('region_na') AND region = 'North America') OR
  (is_member('region_emea') AND region = 'EMEA') OR
  (is_member('region_apac') AND region = 'APAC')

Combining Row and Column Security:

-- View that implements both row and column level security
CREATE OR REPLACE VIEW secured_employee_data AS
SELECT
  employee_id,
  -- Column masking
  CASE WHEN is_member('hr') OR is_member('admins') THEN salary
       ELSE NULL
  END AS salary,
  CASE WHEN is_member('hr') OR is_member('admins') THEN personal_email
       ELSE work_email
  END AS contact_email,
  department,
  title,
  hire_date
FROM employees
WHERE
  -- Row filtering
  is_member('admins') OR
  -- Managers see their department
  (is_member('managers') AND department IN 
    (SELECT department FROM employee_managers WHERE manager_id = current_user())) OR
  -- Employees see only themselves
  (is_member('employees') AND employee_id IN 
    (SELECT employee_id FROM employees WHERE email = current_user()))

Best Practices for Dynamic Views:

Performance Considerations
- Optimize view definitions for performance
- Use appropriate indexing on filtered columns
- Cache frequently accessed views when possible
Maintenance Strategy
- Centralize view definitions
- Document masking rules
- Regular security audits of views
Access Control
- Grant SELECT on views instead of base tables
- Restrict direct table access
- Audit view usage

Key Exam Concepts:

Implementing column-level security with dynamic views
Implementing row-level security with dynamic views
Performance considerations for dynamic views

7. Row and Column-Level Security

Beyond dynamic views, Databricks provides additional mechanisms for implementing row and column-level security.

Unity Catalog Column-Level Security:

Unity Catalog allows direct control over column-level access:

-- Grant access to specific columns
GRANT SELECT ON TABLE customers(customer_id, name, city) TO role_analyst;

-- Deny access to sensitive columns
DENY SELECT ON TABLE customers(credit_card, ssn) TO role_analyst;

Row-Level Security with Row Filters:

-- Create a row filter
CREATE ROW FILTER region_filter ON sales AS
  CASE WHEN is_member('admins') THEN TRUE
       WHEN is_member('na_sales') AND region = 'North America' THEN TRUE
       WHEN is_member('emea_sales') AND region = 'EMEA' THEN TRUE
       ELSE FALSE
  END;

-- Apply the row filter to a role
ALTER TABLE sales ADD ROW FILTER region_filter TO role_regional_analyst;

Column-Level Security with Column Masks:

-- Create a column mask for PII
CREATE MASK pii_mask ON TABLE customers COLUMN email AS
  CASE WHEN is_member('admins') THEN email
       WHEN is_member('marketing') THEN email
       ELSE CONCAT('user@', SPLIT(email, '@')[1])
  END;

-- Apply the mask to a role
ALTER TABLE customers ALTER COLUMN email SET MASK pii_mask TO role_analyst;

Data Access Patterns:

Different access patterns require different security approaches:

Shared Access Pattern
- Multiple teams access same physical data
- Use dynamic views or row/column controls
- Minimize data duplication
Siloed Access Pattern
- Each team has their own copy of data
- Use table/database level permissions
- Higher storage costs but simpler management
Hybrid Access Pattern
- Sensitive data is siloed
- Common data is shared
- Use combination of approaches

Key Exam Concepts:

Unity Catalog security features
Row filters and column masks
Choosing appropriate access patterns

8. Quiz: Data Modeling and Security Concepts

Let’s test your understanding of today’s material with a short quiz:

In the medallion architecture, which layer typically contains data that has been cleansed and standardized but not yet aggregated? a) Bronze layer b) Silver layer c) Gold layer d) Platinum layer
Which SCD type maintains the complete history of dimension changes by creating new records when attributes change? a) SCD Type 0 b) SCD Type 1 c) SCD Type 2 d) SCD Type 3
When implementing data quality checks in Delta Live Tables, which constraint mode would you use to fail the entire pipeline if any record violates the constraint? a) expect_or_drop b) expect_or_fail c) expect_or_quarantine d) expect_all_or_none
Which of the following is NOT one of the four main areas of data governance? a) Data Security b) Data Lineage c) Data Privacy d) Data Transformation
What is the primary purpose of a dynamic view in Databricks? a) To improve query performance through materialization b) To automatically update when source data changes c) To implement row and column-level security based on user attributes d) To create temporary views of data that expire after a session
In Unity Catalog, which of the following represents the correct hierarchy (from highest to lowest)? a) Catalog → Schema → Table b) Database → Catalog → Table c) Workspace → Schema → Table d) Catalog → Table → Column
Which method provides the most efficient way to mask sensitive columns for different user groups? a) Creating separate tables for each user group b) Using dynamic views with conditional logic c) Creating column masks in Unity Catalog d) Encrypting sensitive columns in the base table
Which SCD implementation would be most appropriate for tracking customer address changes while maintaining historical records? a) SCD Type 0 b) SCD Type 1 c) SCD Type 2 d) SCD Type 3

Answers and Explanations:

b) Silver layer - The silver layer contains data that has been cleansed, validated, and standardized, but has not yet been aggregated or transformed for specific business use cases (which happens in the gold layer).
c) SCD Type 2 - Type 2 SCDs create new records when dimension attributes change, typically using start and end dates or current/historical flags to track the complete history.
b) expect_or_fail - The expect_or_fail constraint causes the entire pipeline to fail if any record violates the specified condition, which is useful for critical data quality checks.
d) Data Transformation - The four main areas of data governance are Data Security, Data Governance (metadata management), Compliance, and Privacy. Data Transformation is part of data processing, not governance.
c) To implement row and column-level security based on user attributes - Dynamic views in Databricks are primarily used to implement security controls that adjust what data is visible based on who is viewing it.
a) Catalog → Schema → Table - In Unity Catalog, the hierarchy is Catalog at the top, containing Schemas (also called Databases), which contain Tables and other objects.
c) Creating column masks in Unity Catalog - Column masks in Unity Catalog provide the most efficient and maintainable way to implement column-level security across multiple users and roles.
c) SCD Type 2 - SCD Type 2 is ideal for tracking customer address changes over time while maintaining the complete history, which is important for historical analysis and compliance.

Key Takeaways

We covered essential data modeling and security concepts in Databricks:

Medallion Architecture: Understanding the bronze, silver, and gold layers and how data flows through them
Data Quality Enforcement: Techniques for ensuring data quality at each layer, including constraints and expectations
Slowly Changing Dimensions: Implementing different SCD types to handle historical dimension changes
Security Fundamentals: The four pillars of data governance and how Databricks implements security
Dynamic Views: Creating views that implement row and column-level security based on user attributes
Advanced Security: Using Unity Catalog features for fine-grained access control

Monitoring, Testing & Deployment

1. Spark UI Performance Analysis

The Spark UI is the primary tool for analyzing and troubleshooting Spark application performance. Understanding how to interpret the information it provides is crucial for optimizing data pipelines.

Accessing Spark UI in Databricks:

The Spark UI is available in multiple ways:

Through the Databricks UI via the cluster details page
Directly via the Spark UI tab in a notebook
Through the History Server for completed applications

Key Components of the Spark UI:

Jobs Tab: The Jobs tab shows high-level execution information for each job:

Job submission time and duration
Number of stages and tasks
Status (running, succeeded, failed)
Action that triggered the job (e.g., collect(), count(), save())

When analyzing the Jobs tab, look for:

Long-running jobs that might need optimization
Failed jobs and their error messages
Skew in stage durations within a job

Stages Tab: The Stages tab provides deeper insights into each stage:

Task distribution and skew
Executor utilization
Data read/write metrics
Shuffle metrics

Critical metrics to examine:

Duration Distribution: Identifies skewed tasks
Shuffle Read/Write: High values indicate potential bottlenecks
Spill Metrics: Memory pressure indicators
Input/Output Metrics: Data volume processed

Executors Tab: The Executors tab shows resource utilization across the cluster:

Memory usage (storage and execution)
Disk usage
Task metrics per executor
GC (Garbage Collection) time

Key indicators of problems:

High GC time relative to execution time
Uneven task distribution
Excessive memory spills
Executor failures

Storage Tab: The Storage tab displays information about cached data:

RDD and DataFrame caching
Memory vs. disk storage
Cache hit/miss ratios

Environment Tab: Contains configuration settings for the Spark application:

Spark properties
JVM properties
System properties
Classpath entries

SQL Tab: For Spark SQL and DataFrame operations:

Query execution plans
SQL metrics
Active sessions

Common Performance Issues and Indicators:

1. Data Skew:

Symptoms: Wide variance in task durations within a stage
Solutions: Salting keys, repartitioning, broadcast joins

2. Shuffle Bottlenecks:

Symptoms: High shuffle read/write time, spills to disk
Solutions: Reduce shuffles, optimize join order, increase shuffle partitions

3. Memory Pressure:

Symptoms: Out of memory errors, high GC time
Solutions: Increase executor memory, optimize caching strategy

4. Inefficient Query Plans:

Symptoms: Unexpected join strategies, inefficient stage ordering
Solutions: Query optimization, hint usage, schema optimization

Key Exam Concepts:

Interpreting metrics in different Spark UI tabs
Identifying common performance issues
Understanding the relationship between jobs, stages, and tasks

2. Monitoring Streaming Jobs and SLAs

Streaming jobs require continuous monitoring to ensure they meet performance SLAs and operate reliably.

Key Structured Streaming Metrics:

Processing Rate Metrics:

Input Rate: Records processed per second
Processing Rate: Rate at which records are processed
Batch Duration: Time to process each micro-batch

Latency Metrics:

End-to-End Latency: Time from data arrival to processing completion
Trigger Processing Time: Time spent processing each trigger
Scheduling Delay: Time between triggers

Operational Metrics:

Input Row Count: Number of records in each batch
State Info: Size of maintained state
Watermark Delay: Current watermark position

Monitoring Streaming Jobs in Databricks:

1. Structured Streaming UI: Available through the Spark UI, provides:

Input and processing rates over time
Batch processing times
Operation durations within each batch

2. Streaming Query Metrics:

# Get active streaming queries
active_streams = spark.streams.active

# For each active stream
for stream in active_streams:
    # Get recent progress metrics
    progress = stream.recentProgress
    
    # Get current status
    status = stream.status
    
    # Get specific metrics
    latest = progress[-1] if progress else None
    if latest:
        print(f"Query: {stream.name}")
        print(f"Input rate: {latest['inputRowsPerSecond']}")
        print(f"Processing rate: {latest['processedRowsPerSecond']}")
        print(f"Batch duration: {latest['batchDuration']}")

3. Metrics Collection for Long-Term Analysis:

# Function to log streaming metrics to Delta table
def log_streaming_metrics(batch_df, batch_id):
    # Get all active streams
    active_streams = spark.streams.active
    
    # Collect metrics
    metrics = []
    for stream in active_streams:
        progress = stream.lastProgress
        if progress:
            metrics.append({
                "timestamp": datetime.now(),
                "query_id": stream.id.toString(),
                "query_name": stream.name,
                "input_rows_per_second": progress.get("inputRowsPerSecond", 0),
                "processed_rows_per_second": progress.get("processedRowsPerSecond", 0),
                "batch_duration_ms": progress.get("batchDuration", 0),
                "num_input_rows": progress.get("numInputRows", 0),
                "batch_id": batch_id
            })
    
    # Create DataFrame from metrics and write to Delta table
    if metrics:
        metrics_df = spark.createDataFrame(metrics)
        metrics_df.write.format("delta").mode("append").saveAsTable("streaming_metrics")

# Apply to streaming query
query = df.writeStream \
    .foreachBatch(log_streaming_metrics) \
    .outputMode("update") \
    .option("checkpointLocation", "/path/to/checkpoint") \
    .start()

SLA Monitoring and Alerting:

1. Define SLA Metrics:

Throughput SLAs: Minimum processing rate
Latency SLAs: Maximum end-to-end delay
Availability SLAs: Uptime percentage
Cost SLAs: Maximum resources used

2. Implement Monitoring Dashboards:

# Create dashboard for streaming metrics
from pyspark.sql.functions import col, window

# Read metrics table
metrics = spark.read.table("streaming_metrics")

# Aggregations for dashboard
sla_metrics = metrics.groupBy(window("timestamp", "1 hour")) \
    .agg(
        min("processed_rows_per_second").alias("min_rate"),
        max("batch_duration_ms").alias("max_batch_duration"),
        avg("input_rows_per_second").alias("avg_input_rate")
    ) \
    .withColumn("sla_throughput_met", col("min_rate") > 1000) \
    .withColumn("sla_latency_met", col("max_batch_duration") < 60000)

# Write to dashboard table
sla_metrics.write.format("delta").mode("overwrite").saveAsTable("streaming_sla_dashboard")

3. Implement Alerting:

# Function to check SLAs and trigger alerts
def check_slas(batch_df, batch_id):
    # Get latest metrics
    active_streams = spark.streams.active
    for stream in active_streams:
        progress = stream.lastProgress
        if progress:
            # Check throughput SLA
            input_rate = progress.get("inputRowsPerSecond", 0)
            processed_rate = progress.get("processedRowsPerSecond", 0)
            if processed_rate < 1000:  # Example SLA threshold
                # Trigger throughput alert
                send_alert(f"Throughput SLA violated: {processed_rate} rows/sec")
            
            # Check latency SLA
            batch_duration = progress.get("batchDuration", 0)
            if batch_duration > 60000:  # Example SLA threshold (60 seconds)
                # Trigger latency alert
                send_alert(f"Latency SLA violated: {batch_duration} ms")

# Helper function to send alerts
def send_alert(message):
    # Integration with alerting system (email, Slack, etc.)
    print(f"ALERT: {message}")
    # In production, integrate with actual alerting systems

Key Exam Concepts:

Critical metrics for monitoring streaming jobs
Methods for collecting and analyzing streaming metrics
Implementing SLA monitoring and alerting

3. Ganglia UI and Cluster UI Interpretation

Beyond the Spark UI, Databricks provides additional interfaces for monitoring cluster resources and overall performance.

Ganglia UI:

Ganglia is a distributed monitoring system for clusters that collects and visualizes key system metrics:

Cluster-Level Metrics:

CPU utilization across the cluster
Memory usage patterns
Network I/O
Disk I/O

Node-Level Metrics:

Per-node CPU usage
Memory consumption
Swap usage
Network traffic

Interpreting Ganglia Metrics:

1. CPU Metrics:

High overall CPU usage: May indicate compute-bound workload
Uneven CPU usage: Potential data skew or improper resource allocation
High CPU iowait: I/O bottlenecks

2. Memory Metrics:

Increasing memory usage: Potential memory leak or insufficient resources
High swap usage: Memory pressure, need to increase executor memory
Memory fluctuations: Inefficient caching or garbage collection issues

3. Network Metrics:

High network traffic: Excessive shuffling or data movement
Network spikes: Broadcast operations or large data transfers
Network bottlenecks: May require cluster network optimization

4. Disk Metrics:

High disk I/O: Spilling to disk, inefficient caching
Disk space utilization: Storage constraints
Disk latency: Storage performance issues

Databricks Cluster UI:

The Databricks Cluster UI provides information specific to the Databricks environment:

Cluster Configuration:

Node types and counts
Databricks Runtime version
Autoscaling settings
Cluster policies

Driver and Executor Logs:

Standard output/error logs
Spark application logs
Initialization script logs
Cluster event logs

Cluster Metrics:

Cluster load
Worker status
Autoscaling events
Termination reasons

Common Issues and Signs:

1. Resource Underutilization:

Symptoms: Low CPU/memory usage across cluster
Solution: Rightsize cluster, reduce worker count, use node types with fewer resources

2. Resource Overutilization:

Symptoms: Consistently high CPU/memory, frequent task failures
Solution: Increase cluster size, enable autoscaling, use larger node types

3. Imbalanced Cluster:

Symptoms: Some nodes heavily loaded, others idle
Solution: Review partitioning strategy, check for data skew

4. Unstable Cluster:

Symptoms: Frequent node failures, job failures
Solution: Check initialization scripts, review worker logs, validate dependencies

Key Exam Concepts:

Differentiating between Spark UI, Ganglia UI, and Cluster UI
Interpreting system-level metrics in Ganglia
Correlating application behavior with system metrics

4. Hands-on: Debugging Performance Issues

Let’s walk through a practical approach to identifying and solving common performance issues in Databricks.

Scenario: Investigating a Slow-Running ETL Pipeline

You’ve received alerts that an important ETL pipeline is taking significantly longer than usual. Let’s go through a systematic approach to debug and optimize it.

Step 1: Identify Bottlenecks in Spark UI

First, examine the Jobs tab to find the slowest jobs:

Look for jobs with long duration
Check if the job duration has increased over time
Identify stages within those jobs that are taking the most time

Sample Job Analysis:
- Job 4: 45 minutes (normally takes 10 minutes)
  - Stage 10: 40 minutes
  - Stage 11: 5 minutes

Step 2: Analyze Slow Stages

For the slowest stage (Stage 10), examine:

Task distribution and skew
Shuffle read/write metrics
Executor utilization

Stage 10 Analysis:
- 1000 total tasks
- Task duration: min=5s, median=20s, max=35min
- Significant task skew detected
- Large shuffle write: 200GB

Step 3: Check Ganglia for Resource Utilization

Look at Ganglia metrics during the slow period:

CPU usage pattern
Memory utilization
Disk I/O activity
Network traffic

Ganglia Findings:
- Memory utilization near maximum
- High disk I/O on several nodes
- GC activity increased
- Network traffic normal

Step 4: Examine Query Plan for Inefficiencies

For SQL operations, analyze the query plan:

Check join strategies (broadcast vs. sort-merge)
Look for cartesian products or expensive operations
Verify partition pruning is happening

# Examine the query plan
slow_query.explain(True)

# Sample output showing inefficient join:
# == Physical Plan ==
# *(5) SortMergeJoin [large_key], [large_key]
# :- *(2) Sort [large_key ASC], false, 0
# :  +- Exchange hashpartitioning(large_key, 200)
# :     +- *(1) Filter (isnotnull(large_key))
# :        +- Scan Delta [large_table]
# +- *(4) Sort [large_key ASC], false, 0
#    +- Exchange hashpartitioning(large_key, 200)
#       +- *(3) Filter (isnotnull(large_key))
#          +- Scan Delta [other_large_table]

Step 5: Implement Targeted Optimizations

Based on the analysis, implement specific optimizations:

For Data Skew:

# Add salting to skewed key
from pyspark.sql.functions import rand, col

# Instead of joining directly on skewed_key
df = df.withColumn("salted_key", concat(col("skewed_key"), (rand() * 10).cast("int").cast("string")))

# Join on salted key
result = df.join(other_df.withColumn("salted_key", 
                                     explode(array([lit(i) for i in range(10)]))
                                     .withColumn("salted_key", 
                                                concat(col("skewed_key"), col("salted_key").cast("string")))), 
                 "salted_key")

For Inefficient Joins:

# Use broadcast join for small tables
from pyspark.sql.functions import broadcast

# Before: Regular join causing shuffle
result = large_df.join(small_df, "key")

# After: Broadcast the small table
result = large_df.join(broadcast(small_df), "key")

For Memory Pressure:

# Repartition to better distribute data
df = df.repartition(500, "key")

# Control caching strategy
df.cache()  # Default caching
# or
df.persist(StorageLevel.MEMORY_AND_DISK)  # Spill to disk if needed

For Inefficient I/O:

# Add appropriate file size optimization 
df.write.option("maxRecordsPerFile", 1000000).save("/path")

# Add partitioning for query efficiency
df.write.partitionBy("date", "region").save("/path")

Step 6: Monitor Improvements

After implementing optimizations:

Re-run the job and compare metrics
Validate performance improvements
Document findings and solutions for future reference

Key Debugging Approaches:

Systematic Investigation:
- Start broad, then focus on specific issues
- Use multiple monitoring tools for complete picture
- Follow the data path to identify bottlenecks
Data Flow Analysis:
- Understand data volumes at each stage
- Identify where data expands or contracts
- Look for unnecessary shuffles or redundant processing
Resource Utilization Review:
- Match resource allocation to workload requirements
- Identify imbalances in resource utilization
- Consider cost implications of optimizations

Key Exam Concepts:

Systematic approach to performance debugging
Identifying specific optimization techniques for different issues
Correlating symptoms with root causes

5. Databricks Jobs Configuration and Dependencies

Databricks Jobs provide a way to schedule, monitor, and manage production workloads reliably.

Job Components:

1. Tasks:

Basic unit of execution in a job
Can be notebooks, JARs, wheels, or Delta Live Tables pipelines
Have individual configuration and retry policies

2. Job Configuration:

Schedule settings
Cluster configuration
Parameters and environment variables
Notification settings

3. Workflows:

Collection of dependent tasks
Directed Acyclic Graph (DAG) of task dependencies
Sequential or parallel execution paths

Creating Multi-Task Jobs:

Simple Job with Sequential Tasks:

{
  "name": "Customer Data Processing",
  "tasks": [
    {
      "task_key": "ingest_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/ingest_customer_data",
        "base_parameters": {
          "data_date": "2023-05-01"
        }
      },
      "existing_cluster_id": "0923-164819-h73vpgf0"
    },
    {
      "task_key": "transform_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/transform_customer_data",
        "base_parameters": {
          "data_date": "2023-05-01"
        }
      },
      "existing_cluster_id": "0923-164819-h73vpgf0",
      "depends_on": [
        {
          "task_key": "ingest_data"
        }
      ]
    },
    {
      "task_key": "load_analytics",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/load_analytics",
        "base_parameters": {
          "data_date": "2023-05-01"
        }
      },
      "existing_cluster_id": "0923-164819-h73vpgf0",
      "depends_on": [
        {
          "task_key": "transform_data"
        }
      ]
    }
  ]
}

Complex Job with Parallel Tasks:

{
  "name": "Enterprise Data Pipeline",
  "tasks": [
    {
      "task_key": "ingest_raw_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/ingest_raw_data"
      },
      "new_cluster": {
        "spark_version": "11.3.x-scala2.12",
        "node_type_id": "i3.xlarge",
        "num_workers": 4
      }
    },
    {
      "task_key": "process_customer_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/process_customer_data"
      },
      "new_cluster": {
        "spark_version": "11.3.x-scala2.12",
        "node_type_id": "i3.xlarge",
        "num_workers": 2
      },
      "depends_on": [
        {
          "task_key": "ingest_raw_data"
        }
      ]
    },
    {
      "task_key": "process_product_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/process_product_data"
      },
      "new_cluster": {
        "spark_version": "11.3.x-scala2.12",
        "node_type_id": "i3.xlarge",
        "num_workers": 2
      },
      "depends_on": [
        {
          "task_key": "ingest_raw_data"
        }
      ]
    },
    {
      "task_key": "process_order_data",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/process_order_data"
      },
      "new_cluster": {
        "spark_version": "11.3.x-scala2.12",
        "node_type_id": "i3.xlarge",
        "num_workers": 2
      },
      "depends_on": [
        {
          "task_key": "ingest_raw_data"
        }
      ]
    },
    {
      "task_key": "build_analytics",
      "notebook_task": {
        "notebook_path": "/Shared/ETL/build_analytics"
      },
      "new_cluster": {
        "spark_version": "11.3.x-scala2.12",
        "node_type_id": "i3.xlarge",
        "num_workers": 4
      },
      "depends_on": [
        {
          "task_key": "process_customer_data"
        },
        {
          "task_key": "process_product_data"
        },
        {
          "task_key": "process_order_data"
        }
      ]
    }
  ]
}

Job Scheduling Options:

Time-Based Scheduling:
- Fixed schedule using cron expressions
- Periodic intervals (hourly, daily, weekly)
- Timezone configuration
Event-Based Triggers:
- File arrivals
- REST API calls
- Completion of other jobs
Continuous Jobs:
- Always running, handling data as it arrives
- Useful for near real-time processing

Retry and Recovery Policies:

{
  "task_key": "critical_task",
  "notebook_task": {
    "notebook_path": "/Shared/ETL/critical_processing"
  },
  "existing_cluster_id": "0923-164819-h73vpgf0",
  "retry_on_timeout": true,
  "max_retries": 3,
  "min_retry_interval_millis": 60000,
  "retry_on_timeout": true
}

Parameter Passing Between Tasks:

# In a notebook task, set output parameters
dbutils.jobs.taskValues.set(key = "customer_count", value = customer_df.count())
dbutils.jobs.taskValues.set(key = "process_date", value = current_date)

# In a dependent task, retrieve values
customer_count = dbutils.jobs.taskValues.get(taskKey = "ingest_data", key = "customer_count")
process_date = dbutils.jobs.taskValues.get(taskKey = "ingest_data", key = "process_date")

Notification and Alerting:

{
  "name": "Production ETL Pipeline",
  "email_notifications": {
    "on_start": ["etl-monitoring@company.com"],
    "on_success": ["etl-monitoring@company.com"],
    "on_failure": ["etl-alerts@company.com", "oncall@company.com"],
    "no_alert_for_skipped_runs": false
  },
  "webhook_notifications": {
    "on_failure": [
      {
        "id": "webhook-id"
      }
    ]
  },
  "tasks": [
    // Task definitions
  ]
}

Key Exam Concepts:

Designing multi-task workflows with appropriate dependencies
Configuring retry and recovery policies for resilient jobs
Setting up monitoring and alerting for job status

6. Databricks CLI and REST API Implementation

The Databricks CLI and REST API provide programmatic ways to interact with Databricks resources, enabling automation and integration with external systems.

Databricks CLI:

Installation and Configuration:

# Install Databricks CLI
pip install databricks-cli

# Configure authentication
databricks configure --token
# Enter host (e.g., https://your-workspace.cloud.databricks.com)
# Enter token

# Verify configuration
databricks workspace ls

Working with Workspace Assets:

# List workspace contents
databricks workspace ls /Shared

# Export notebook
databricks workspace export /Shared/MyNotebook -f JUPYTER -o my_notebook.ipynb

# Import notebook
databricks workspace import my_notebook.ipynb /Shared/ImportedNotebook -l PYTHON -f JUPYTER

# Create directory
databricks workspace mkdirs /Shared/NewDirectory

Managing Jobs:

# List jobs
databricks jobs list

# Get job details
databricks jobs get --job-id 123

# Run a job
databricks jobs run-now --job-id 123

# Create a job from JSON definition
databricks jobs create --json @job_definition.json

Cluster Management:

# List clusters
databricks clusters list

# Start/stop cluster
databricks clusters start --cluster-id 0923-164819-h73vpgf0
databricks clusters terminate --cluster-id 0923-164819-h73vpgf0

# Create a cluster
databricks clusters create --json @cluster_config.json

DBFS Operations:

# List files
databricks fs ls dbfs:/data/

# Copy files to DBFS
databricks fs cp local_file.csv dbfs:/data/

# Move files within DBFS
databricks fs mv dbfs:/data/old.csv dbfs:/data/new.csv

# Remove files
databricks fs rm dbfs:/data/old_data.csv

Databricks REST API:

The REST API enables the same operations with direct HTTP requests:

Authentication:

import requests

# Authentication
headers = {
    "Authorization": f"Bearer {your_access_token}",
    "Content-Type": "application/json"
}

# Base URL
base_url = "https://your-workspace.cloud.databricks.com/api/2.0"

Working with Jobs:

# List jobs
response = requests.get(
    f"{base_url}/jobs/list",
    headers=headers
)
jobs = response.json()

# Run a job
job_id = 123
response = requests.post(
    f"{base_url}/jobs/run-now",
    headers=headers,
    json={"job_id": job_id}
)
run_id = response.json()["run_id"]

# Get run status
response = requests.get(
    f"{base_url}/jobs/runs/get",
    headers=headers,
    params={"run_id": run_id}
)
run_status = response.json()["state"]["life_cycle_state"]

Creating a Job:

# Job definition
job_config = {
    "name": "API Created Job",
    "tasks": [
        {
            "task_key": "main_task",
            "notebook_task": {
                "notebook_path": "/Shared/MyNotebook",
                "base_parameters": {
                    "param1": "value1"
                }
            },
            "new_cluster": {
                "spark_version": "11.3.x-scala2.12",
                "node_type_id": "i3.xlarge",
                "num_workers": 2
            }
        }
    ],
    "schedule": {
        "quartz_cron_expression": "0 0 10 ? * MON-FRI",
        "timezone_id": "America/Los_Angeles"
    }
}

# Create job
response = requests.post(
    f"{base_url}/jobs/create",
    headers=headers,
    json=job_config
)
new_job_id = response.json()["job_id"]

Automating Deployment with CI/CD:

import os
import requests
import json

# Function to deploy a notebook to production
def deploy_notebook(notebook_path, target_path):
    # Export from development workspace
    export_response = requests.get(
        f"{dev_workspace_url}/api/2.0/workspace/export",
        headers=dev_headers,
        params={
            "path": notebook_path,
            "format": "SOURCE"
        }
    )
    
    if export_response.status_code != 200:
        raise Exception(f"Failed to export: {export_response.text}")
    
    # Import to production workspace
    import_response = requests.post(
        f"{prod_workspace_url}/api/2.0/workspace/import",
        headers=prod_headers,
        json={
            "path": target_path,
            "format": "SOURCE",
            "content": export_response.json()["content"],
            "overwrite": True
        }
    )
    
    if import_response.status_code != 200:
        raise Exception(f"Failed to import: {import_response.text}")
    
    print(f"Successfully deployed {notebook_path} to {target_path}")

# Example usage in CI/CD pipeline
if __name__ == "__main__":
    # Get environment variables (set by CI/CD system)
    dev_workspace = os.environ["DEV_WORKSPACE_URL"]
    dev_token = os.environ["DEV_TOKEN"]
    prod_workspace = os.environ["PROD_WORKSPACE_URL"]
    prod_token = os.environ["PROD_TOKEN"]
    
    # Set up headers
    dev_headers = {
        "Authorization": f"Bearer {dev_token}",
        "Content-Type": "application/json"
    }
    prod_headers = {
        "Authorization": f"Bearer {prod_token}",
        "Content-Type": "application/json"
    }
    
    # Deploy notebooks
    deploy_notebook("/Dev/ETL/ingest_data", "/Prod/ETL/ingest_data")
    deploy_notebook("/Dev/ETL/transform_data", "/Prod/ETL/transform_data")
    
    # Update job configuration
    # ... similar code to update job definitions

Key Exam Concepts:

Using the Databricks CLI for common operations
Implementing REST API calls for automation
Integrating Databricks with CI/CD workflows

7. CI/CD Patterns for Databricks

Implementing continuous integration and continuous deployment (CI/CD) for Databricks requires specific patterns and tools.

CI/CD Components for Databricks:

1. Version Control Integration:

Databricks Repos for Git integration
Notebook source control
External Git repositories

2. Development Environments:

Development workspace
Testing workspace
Production workspace

3. Automated Testing:

Unit tests for UDFs and functions
Integration tests for data pipelines
Performance tests for production readiness

4. Deployment Automation:

Automated deployment scripts
Workspace-to-workspace promotion
Configuration management

Common CI/CD Patterns:

Pattern 1: Notebook-Based Development and Deployment

Development:
- Develop in interactive notebooks
- Use Databricks Repos for version control
- Parameterize notebooks for different environments
Testing:
- Run automated tests in test workspace
- Validate data quality and transformations
- Test with representative data volumes
Deployment:
- Promote notebooks to production workspace
- Update job definitions
- Configure for production parameters

Implementation with Databricks Repos:

# Clone repository in Databricks
dbutils.widgets.get("Clone Repository")

# Run in Repos UI

# Run tests within notebook
def test_transformation():
    test_data = spark.createDataFrame([
        (1, "test"),
        (2, "test2")
    ], ["id", "name"])
    
    result = transform_function(test_data)
    
    # Assertions
    assert result.count() == 2
    assert "transformed_column" in result.columns

# Test execution
test_transformation()

Pattern 2: External CI/CD with API Integration

Development:
- Develop code in external IDE
- Store code in Git repository
- Use standard CI/CD tools (Jenkins, GitHub Actions, etc.)
Testing:
- Run local unit tests
- Deploy to test workspace via API
- Run integration tests in Databricks environment
Deployment:
- Deploy artifacts to production via API
- Update job configurations
- Manage environment-specific parameters

GitHub Actions Workflow Example:

name: Databricks CI/CD

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: 3.8
    
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install pytest databricks-cli
    
    - name: Run unit tests
      run: pytest tests/

  deploy-to-test:
    needs: test
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    
    - name: Deploy to Test
      env:
        DATABRICKS_HOST: $
        DATABRICKS_TOKEN: $
      run: |
        python deployment/deploy_to_databricks.py --environment test
    
    - name: Run Integration Tests
      env:
        DATABRICKS_HOST: $
        DATABRICKS_TOKEN: $
      run: |
        python tests/integration/run_integration_tests.py

  deploy-to-prod:
    needs: deploy-to-test
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    
    - name: Deploy to Production
      env:
        DATABRICKS_HOST: $
        DATABRICKS_TOKEN: $
      run: |
        python deployment/deploy_to_databricks.py --environment prod

Pattern 3: Delta Live Tables (DLT) CI/CD

Development:
- Develop DLT pipeline in development workspace
- Version control with Repos
- Define expectations and quality rules
Testing:
- Deploy to test DLT pipeline
- Validate data quality and transformations
- Test with representative data volumes
Deployment:
- Deploy DLT definition to production
- Configure for production data sources and destinations
- Set appropriate SLAs and monitoring

DLT Pipeline Deployment:

# Python script to update DLT pipeline configuration
import requests
import json
import os

def update_dlt_pipeline(pipeline_id, config_updates, workspace_url, token):
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json"
    }
    
    # Get current pipeline config
    get_response = requests.get(
        f"{workspace_url}/api/2.0/pipelines/{pipeline_id}",
        headers=headers
    )
    
    if get_response.status_code != 200:
        raise Exception(f"Failed to get pipeline: {get_response.text}")
    
    current_config = get_response.json()
    
    # Update config
    for key, value in config_updates.items():
        if key in current_config:
            current_config[key] = value
    
    # Update pipeline
    update_response = requests.put(
        f"{workspace_url}/api/2.0/pipelines/{pipeline_id}",
        headers=headers,
        json=current_config
    )
    
    if update_response.status_code != 200:
        raise Exception(f"Failed to update pipeline: {update_response.text}")
    
    print(f"Successfully updated pipeline {pipeline_id}")

# Example usage
if __name__ == "__main__":
    # Environment-specific configurations
    env = os.environ.get("DEPLOY_ENV", "dev")
    
    if env == "dev":
        target_dir = "/development"
        cluster_size = "Small"
    elif env == "test":
        target_dir = "/test"
        cluster_size = "Medium"
    elif env == "prod":
        target_dir = "/production"
        cluster_size = "Large"
    
    # Update pipeline
    update_dlt_pipeline(
        pipeline_id="12345",
        config_updates={
            "name": f"Customer Data Pipeline - {env.upper()}",
            "target": f"{target_dir}/customer_data",
            "configuration": {
                "pipeline.trigger.interval": "1 hour" if env == "prod" else "6 hours"
            },
            "clusters": [
                {
                    "label": "default",
                    "autoscale": {
                        "min_workers": 1,
                        "max_workers": 8 if env == "prod" else 4
                    }
                }
            ]
        },
        workspace_url=os.environ["DATABRICKS_HOST"],
        token=os.environ["DATABRICKS_TOKEN"]
    )

Key Exam Concepts:

Designing CI/CD pipelines for Databricks workloads
Implementing automated testing for data pipelines
Managing environment-specific configurations

8. Quiz: Monitoring and Deployment Concepts

Let’s test your understanding of today’s material with a short quiz:

In the Spark UI, which tab would you examine to identify data skew in a specific stage? a) Jobs b) Stages c) Executors d) SQL
Which metric is most important to monitor for streaming applications to ensure they are keeping up with incoming data? a) Processing rate vs. input rate b) Batch processing time c) Record count d) Total duration
When setting up a multi-task job in Databricks, how do you specify that Task B should only run after Task A completes successfully? a) Set “run_after” parameter in Task B b) Add Task A as a prerequisite in Task B c) Include “depends_on” with Task A’s task_key in Task B d) Configure Task A to trigger Task B
Which Databricks CLI command would you use to programmatically create a job from a JSON configuration file? a) databricks jobs create-from-file job_config.json b) databricks jobs create –json @job_config.json c) databricks jobs import –file job_config.json d) databricks jobs new –config job_config.json
What is a key advantage of using Databricks Repos for CI/CD compared to external Git integration? a) Built-in version control within the Databricks workspace b) Automatic deployment to production c) Better performance for notebook execution d) Support for more Git providers
In Ganglia UI, which metric would best indicate memory pressure on your cluster? a) CPU utilization b) Network bytes in/out c) Swap usage d) Disk I/O
Which REST API endpoint would you use to run an existing Databricks job? a) /api/2.0/jobs/start b) /api/2.0/jobs/execute c) /api/2.0/jobs/run-now d) /api/2.0/jobs/trigger
In a CI/CD pipeline for Databricks, which approach is recommended for handling environment-specific configurations? a) Hard-code configurations in notebooks for each environment b) Use different branches for different environments c) Parameterize notebooks and jobs with environment-specific values d) Duplicate workspaces for each environment

Answers and Explanations:

b) Stages - The Stages tab provides detailed information about task distribution and duration, which helps identify skew where some tasks take significantly longer than others.
a) Processing rate vs. input rate - Monitoring the relationship between processing rate and input rate is crucial to ensure the streaming application can keep up with incoming data; if input rate consistently exceeds processing rate, the application will fall behind.
c) Include “depends_on” with Task A’s task_key in Task B - In Databricks Jobs, task dependencies are specified using the “depends_on” parameter with the dependent task’s task_key.
b) databricks jobs create –json @job_config.json - This is the correct syntax for creating a job from a JSON configuration file using the Databricks CLI.
a) Built-in version control within the Databricks workspace - Databricks Repos provides native Git integration directly within the workspace, allowing developers to version control notebooks without leaving the Databricks environment.
c) Swap usage - High swap usage indicates that the system is under memory pressure and is using disk space as virtual memory, which significantly impacts performance.
c) /api/2.0/jobs/run-now - This is the correct endpoint for triggering an existing job to run via the Databricks REST API.
c) Parameterize notebooks and jobs with environment-specific values - This approach allows the same code to be deployed across environments while adapting configurations based on the target environment, which is a best practice for CI/CD.

Key Takeaways

We covered essential monitoring, testing, and deployment concepts in Databricks:

Spark UI Analysis: Understanding how to interpret Spark UI metrics to identify performance bottlenecks
Streaming Monitoring: Techniques for monitoring streaming jobs and ensuring they meet SLAs
Cluster Performance: How to use Ganglia UI and Cluster UI to analyze resource utilization
Performance Debugging: A systematic approach to identifying and resolving performance issues
Job Configuration: Setting up multi-task jobs with dependencies for complex workflows
Databricks CLI and REST API: Programmatic interfaces for automation and integration
CI/CD Patterns: Implementing continuous integration and deployment for Databricks workloads

These concepts are crucial for managing, monitoring, and deploying production-grade data pipelines in Databricks. Tomorrow, we’ll review all the material covered so far and focus on comprehensive practice exams to prepare for the certification.

Comprehensive Review & Final Assessment

1. Comprehensive Review of Key Exam Topics

Let’s start by reviewing the essential concepts from each exam section:

Section 1: Databricks Tooling

Delta Lake Core Concepts:

Transaction log is the foundation of ACID properties in Delta Lake
Delta Lake maintains file-level metadata for efficient querying
Optimistic concurrency control manages multiple writers
Time travel enables querying historical versions of tables

Performance Optimization Techniques:

Partitioning organizes data into directories based on column values
Z-Ordering co-locates related data within files for faster queries
Bloom filters accelerate point lookups for high-cardinality columns
File size optimization balances query parallelism and overhead

Delta Lake Operations:

OPTIMIZE compacts small files into larger ones
VACUUM removes files no longer referenced by the transaction log
MERGE provides efficient upsert capabilities
Delta cloning (shallow/deep) creates copies with minimal storage overhead

Section 2: Data Processing

Batch Processing Concepts:

Transformations (lazy) vs. actions (eager) in Spark
Shuffle operations impact performance and require tuning
Partition management affects parallelism and efficiency
Caching strategies for iterative processing

Streaming Processing:

Structured Streaming treats stream as an unbounded table
Trigger types control batch frequency
Output modes (append, update, complete) determine write behavior
Watermarking manages state for aggregations and joins

Incremental Processing:

Change Data Feed (CDF) captures row-level changes
APPLY CHANGES simplifies CDC processing
Incremental reads optimize processing of new data
Stream-static joins combine streaming and batch data

Section 3: Data Modeling

Medallion Architecture:

Bronze layer: Raw, unprocessed data
Silver layer: Cleansed, validated data
Gold layer: Business-ready, aggregated data

Data Quality:

Schema enforcement prevents type mismatches
Constraints enforce business rules
Expectations in DLT handle invalid data
Quality monitoring ensures ongoing compliance

Slowly Changing Dimensions:

Type 0: No changes tracked
Type 1: Overwrite with latest values
Type 2: Maintain historical records with validity periods
Type 6: Hybrid approach combining multiple methods

Section 4: Security & Governance

Security Fundamentals:

Unity Catalog provides three-level namespace (catalog.schema.table)
Access control at multiple levels (object, column, row)
Dynamic views implement custom security logic
Securable objects include tables, views, functions, and more

Data Masking:

Column-level masking for sensitive data
Row-level filtering based on user attributes
Role-based access control integration
Audit logging for compliance

Section 5: Monitoring & Logging

Performance Analysis:

Spark UI provides job, stage, and task metrics
Ganglia UI shows cluster resource utilization
Identify common issues: data skew, shuffle bottlenecks, memory pressure
Troubleshooting methodology for performance problems

Streaming Monitoring:

Processing rate vs. input rate
Watermark progression
State size management
End-to-end latency

Section 6: Testing & Deployment

Job Orchestration:

Multi-task jobs with dependencies
Retry and recovery policies
Parameter passing between tasks
Notification and alerting

Automation:

Databricks CLI for common operations
REST API for programmatic interaction
CI/CD patterns for Databricks workloads
Environment management (dev/test/prod)

2. Half-Length Practice Exam (30 Questions)

Now, let’s test your knowledge with a half-length practice exam covering all sections proportionally.

Instructions:

Time limit: 60 minutes
30 multiple-choice questions
All sections of the exam are covered
Select the best answer for each question

Question 1: What is the primary purpose of the transaction log in Delta Lake?

A) To store table schema information
B) To track all changes made to the table
C) To optimize query performance
D) To manage access control permissions

Question 2: When implementing Z-ordering on a Delta table, which columns should you prioritize?

A) High-cardinality columns used in JOIN conditions
B) Low-cardinality columns used in filtering
C) Columns with the largest data sizes
D) Timestamp columns regardless of query patterns

Question 3: What happens when you execute a MERGE operation on a Delta table with a condition that matches multiple source records to the same target record?

A) The operation succeeds and applies all matches sequentially
B) The operation fails with a runtime error about duplicate matches
C) The operation succeeds but only applies the first match
D) The operation requires a “matchedBy” clause to resolve duplicates

Question 4: Which partition strategy would be most efficient for a table that is frequently queried with filters on both “date” (daily, high cardinality) and “region” (10 distinct values)?

A) Partition by date only
B) Partition by region only
C) Partition by both date and region
D) Do not partition but use Z-ORDER by date, region

Question 5: What is the main difference between repartition() and coalesce() in Spark?

A) repartition() can only decrease partitions, coalesce() can only increase them
B) repartition() performs a full shuffle, coalesce() avoids shuffling when reducing partitions
C) repartition() works with any data type, coalesce() only works with numeric columns
D) repartition() preserves ordering, coalesce() does not preserve ordering

Question 6: In Structured Streaming, which watermark configuration would allow processing of events that arrive up to 2 hours late?

A) .withWatermark("eventTime", "2 hours")
B) .withWatermark("eventTime", "120 minutes")
C) .option("watermarkDelay", "2 hours")
D) Both A and B are correct

Question 7: Which output mode in Structured Streaming requires an aggregation and updates the entire result table when triggered?

A) Append mode
B) Update mode
C) Complete mode
D) Overwrite mode

Question 8: When implementing a stream-static join, what is the best practice for the static DataFrame?

A) Cache it before joining
B) Broadcast it if it’s small enough
C) Repartition it to match the streaming DataFrame’s partitioning
D) Checkpoint it to ensure consistency

Question 9: In the medallion architecture, where should deduplication of records typically occur?

A) In the bronze layer
B) During transformation from bronze to silver
C) During transformation from silver to gold
D) In all layers independently

Question 10: Which constraint type in Delta Live Tables will drop records that don’t meet the condition rather than failing the entire batch?

A) expect
B) expect_or_drop
C) expect_or_fail
D) expect_or_null

Question 11: Which SCD type would you implement if you need to track the complete history of customer address changes, including when each address was valid?

A) SCD Type 0
B) SCD Type 1
C) SCD Type 2
D) SCD Type 3

Question 12: What type of join would be most efficient when joining a large fact table with a small dimension table in Spark?

A) Shuffle hash join
B) Broadcast join
C) Sort-merge join
D) Nested loop join

Question 13: Which approach is most efficient for handling late-arriving data in a streaming application that computes hourly aggregations?

A) Use a larger trigger interval to wait for late data
B) Implement watermarking with allowed lateness
C) Store raw events and recompute aggregations periodically
D) Use foreachBatch to manually handle late records

Question 14: In Delta Lake, what happens when you run VACUUM on a table?

A) It physically removes data files not referenced in the current table version
B) It optimizes the metadata for faster queries
C) It compacts small files into larger ones
D) It removes old versions from the transaction log

Question 15: Which feature of Delta Lake would you use to implement incremental processing based on changes to source data?

A) Time travel
B) MERGE operation
C) Change Data Feed
D) COPY INTO command

Question 16: Which approach provides the most secure method for masking sensitive columns for different user groups in Databricks?

A) Creating separate tables for each user group
B) Using dynamic views with conditional expressions
C) Creating column masks in Unity Catalog
D) Encrypting sensitive columns in the base table

Question 17: What is the primary benefit of using Unity Catalog in Databricks?

A) Improved query performance through optimization
B) Unified governance across workspaces and clouds
C) Automatic data quality enforcement
D) Built-in data lineage visualization

Question 18: In the Spark UI, which tab would you examine to identify if a stage has skewed task durations?

A) Jobs tab
B) Stages tab
C) Executors tab
D) Storage tab

Question 19: Which metric is most important to monitor in a streaming application to ensure it’s keeping up with incoming data?

A) Processing rate vs. input rate
B) Batch duration over time
C) Number of records processed
D) Memory usage of executors

Question 20: When designing a multi-task job in Databricks, what’s the best way to pass data between tasks?

A) Write temporary tables that subsequent tasks read
B) Use dbutils.jobs.taskValues API
C) Configure job parameters that all tasks access
D) Store data in DBFS for shared access

Question 21: Which Databricks CLI command would you use to export a notebook to the local file system?

A) databricks workspace export
B) databricks workspace get
C) databricks notebooks download
D) databricks fs cp

Question 22: What is a shallow clone in Delta Lake?

A) A copy of the table data with a reduced schema
B) A reference to the original table’s files with its own transaction log
C) A sampled subset of the original table
D) A copy with only the most recent version of the data

Question 23: Which storage level is most appropriate for a DataFrame that is reused multiple times but too large to fit entirely in memory?

A) MEMORY_ONLY
B) MEMORY_AND_DISK
C) DISK_ONLY
D) OFF_HEAP

Question 24: What is the primary purpose of using foreachBatch in Structured Streaming instead of standard output sinks?

A) It provides higher throughput for large batches
B) It allows applying different operations to each micro-batch
C) It eliminates the need for checkpointing
D) It guarantees exactly-once processing semantics

Question 25: When implementing CDC processing with Delta Lake, which operation simplifies applying inserts, updates, and deletes from a CDC source?

A) MERGE INTO
B) APPLY CHANGES INTO
C) UPDATE
D) UPSERT

Question 26: Which technique would you use to optimize a Delta table for queries that frequently filter on a high-cardinality column?

A) Partition by that column
B) Z-ORDER by that column
C) Create a bloom filter index on that column
D) Both B and C are recommended

Question 27: In a CI/CD pipeline for Databricks, what is the best approach for managing environment-specific configurations?

A) Hard-code configurations in notebooks for each environment
B) Use different Git branches for different environments
C) Parameterize notebooks and jobs with environment-specific values
D) Duplicate workspaces for each environment

Question 28: Which performance issue would be indicated by high shuffle read/write metrics in the Spark UI?

A) Inefficient partition pruning
B) Poor broadcast variable utilization
C) Excessive data movement across the cluster
D) Memory pressure on executors

Question 29: What does the “dataChange” flag in a Delta write operation control?

A) Whether schema validation is enforced
B) Whether the write triggers downstream processing
C) Whether statistics are computed for the new data
D) Whether partition discovery is performed

Question 30: Which approach is most efficient for updating a small number of records in a large Delta table?

A) Overwrite the entire table with updated data
B) Use MERGE with a targeted condition
C) DELETE old records and INSERT new versions
D) Create a new table with updated records and UNION with unchanged records

Answer Key and Explanations:

B - The transaction log records all changes (adds, removes, updates) to the table, enabling ACID properties and time travel.
A - Z-ordering is most effective on high-cardinality columns used in JOIN conditions, as it co-locates related data and enables efficient data skipping.
B - MERGE operations require that each source record matches at most one target record; multiple matches cause a runtime error.
C - For frequent filtering on both columns, partitioning by both date and region would create an efficient directory structure that enables partition pruning on either dimension.
B - repartition() performs a full shuffle to distribute data evenly, while coalesce() minimizes data movement when reducing partition count.
D - Both expressions are equivalent and configure a watermark that allows processing of events up to 2 hours late.
C - Complete mode requires an aggregation and outputs the entire result table each time, including all keys and updated values.
B - Broadcasting the static DataFrame (if small enough) eliminates the need for shuffling and improves join performance.
B - Deduplication typically occurs during the bronze to silver transformation, as silver tables should contain clean, validated, and deduplicated data.
B - expect_or_drop constraints in DLT will filter out records that don’t meet the condition without failing the entire pipeline.
C - SCD Type 2 tracks historical changes by creating new records with start and end dates or current/historical flags.
B - Broadcast join is most efficient when joining with a small dimension table, as it eliminates shuffling by broadcasting the small table to all executors.
B - Watermarking with allowed lateness allows the stream to process late data within the defined threshold while enabling state cleanup.
A - VACUUM physically removes data files that are not referenced in the current or recent versions of the table.
C - Change Data Feed tracks row-level changes and enables incremental processing based on what has changed in the source.
C - Column masks in Unity Catalog provide a centralized, efficient way to implement column-level security across users and roles.
B - Unity Catalog provides unified governance across workspaces and clouds with a consistent security model and metadata management.
B - The Stages tab shows task distribution and duration, making it easy to identify skewed tasks where some take significantly longer than others.
A - Comparing processing rate to input rate reveals whether the streaming application can keep up with incoming data or is falling behind.
B - The dbutils.jobs.taskValues API allows tasks to set and retrieve values, enabling direct data passing between dependent tasks.
A - databricks workspace export is the correct command to export a notebook from Databricks to the local file system.
B - A shallow clone creates a reference to the original table’s files but maintains its own transaction log, allowing independent modifications.
B - MEMORY_AND_DISK is appropriate for DataFrames that may not fit entirely in memory, as it will spill to disk as needed.
B - foreachBatch gives you complete control over how each micro-batch is processed, enabling complex logic that isn’t possible with standard sinks.
B - APPLY CHANGES INTO is specifically designed for CDC processing, automatically handling inserts, updates, and deletes based on operation type.
D - For high-cardinality columns, using both Z-ORDER and bloom filter indices provides optimal query performance through data co-location and filtering.
C - Parameterizing notebooks and jobs allows the same code to be deployed across environments while adapting configurations based on the target environment.
C - High shuffle read/write metrics indicate excessive data movement across the cluster, often caused by operations like join, groupBy, or repartition.
B - The dataChange flag indicates whether the write operation should trigger downstream processing like table notifications or streaming queries.
B - MERGE with a targeted condition efficiently updates specific records without rewriting the entire table or requiring multiple operations.

3. Review of Practice Exam Results

Now let’s review the practice exam results and focus on areas that need additional attention. For each section where questions were missed, we’ll provide targeted reinforcement.

Common Challenge Areas:

Delta Lake Optimization Choices:
- Understanding when to partition vs. Z-order
- Optimal file size considerations
- Bloom filter use cases
Streaming Concepts:
- Watermark configuration and impact
- State management in streaming queries
- Output mode selection criteria
Performance Troubleshooting:
- Identifying root causes from Spark UI metrics
- Resolving data skew issues
- Optimizing shuffle operations
Advanced Delta Operations:
- CDC implementation patterns
- MERGE operation constraints
- Time travel and versioning
Job Orchestration:
- Multi-task dependency configuration
- Parameter passing between tasks
- Error handling and recovery strategies

4. Exam Strategy Discussion

Time Management:

The exam consists of 60 questions to be completed in 120 minutes, giving you 2 minutes per question on average.

Recommended approach:

First pass (60-70 minutes):
- Answer questions you’re confident about immediately
- Mark questions you’re unsure about for review
- Spend no more than 1.5 minutes per question
Second pass (30-40 minutes):
- Return to marked questions
- Eliminate obviously incorrect options
- Apply deductive reasoning for complex questions
Final review (10-20 minutes):
- Ensure all questions are answered
- Check for misread questions
- Verify answers for questions you were uncertain about

Question Types and Strategies:

Scenario-based questions:

Read the scenario carefully to identify the specific problem
Look for keywords that indicate constraints or requirements
Eliminate options that violate stated constraints
Select the option that best addresses all requirements

Code-based questions:

Trace through the code mentally to understand its purpose
Focus on edge cases and potential errors
Look for inefficient patterns or non-optimal approaches
Consider scalability implications

Best practice questions:

Apply Databricks-recommended approaches
Consider trade-offs between performance, cost, and complexity
Recognize patterns from practical experience

Troubleshooting questions:

Identify symptoms and potential causes
Look for the most direct solution that addresses the root cause
Eliminate solutions that only address symptoms

Exam Psychology:

Stay calm: Anxiety reduces cognitive performance
Trust your preparation: You’ve covered all the material systematically
Don’t second-guess: Your first instinct is often correct
No negative marking: Answer all questions, even if unsure

5. Final Concept Clarification

Let’s address some commonly confused concepts and provide final clarification:

Delta Lake Table Types:

Managed tables:
- Metadata stored in the metastore
- Data stored in the default location (dbfs:/user/hive/warehouse/…)
- Dropping the table deletes both metadata and data
External tables:
- Metadata stored in the metastore
- Data stored in a specified location
- Dropping the table deletes only metadata, not data

Streaming vs. Batch Processing:

	Streaming	Batch
Data scope	Unbounded	Bounded
Processing model	Continuous or micro-batch	Scheduled or triggered
State management	Required for aggregations	Generally stateless
Latency	Low (seconds to minutes)	High (minutes to hours)
Fault tolerance	Checkpointing	Job restart
Use cases	Real-time dashboards, alerts	Historical analysis, reporting

SCD Implementation Patterns:

Type 1 (Overwrite):

# Using MERGE
deltaTable.alias("target").merge(
    updates.alias("updates"),
    "target.key = updates.key"
).whenMatched().updateAll().execute()

Type 2 (Historical):

# Step 1: Expire current records
deltaTable.alias("target").merge(
    updates.alias("updates"),
    "target.key = updates.key AND target.is_current = true"
).whenMatched().updateExpr({
    "is_current": "false",
    "end_date": "current_date()"
})
  
# Step 2: Insert new versions
.whenNotMatched().insertExpr({
    "key": "updates.key",
    "attribute": "updates.attribute",
    "is_current": "true",
    "start_date": "current_date()",
    "end_date": "null"
}).execute()

Performance Optimization Decision Tree:

Issue: Slow overall job performance
- Check: Stage durations in Spark UI
- If one stage dominates: Focus on that stage
- If multiple stages are slow: Look for common factors
Issue: Skewed tasks within a stage
- Check: Task duration distribution
- Solutions:
  - Repartition with better key
  - Salting for skewed keys
  - AQE skew join optimization
Issue: High shuffle read/write
- Solutions:
  - Reduce partition count
  - Broadcast small tables
  - Optimize join order
  - Pre-aggregate before join
Issue: Out of memory errors
- Solutions:
  - Increase executor memory
  - Reduce parallelism
  - Cache selectively
  - Use disk persistence for large datasets

Security Implementation Best Practices:

Table Access Control:
- Grant permissions at database/schema level where possible
- Use role-based access control rather than individual users
- Follow principle of least privilege
Column Security:
- Use dynamic views for complex masking logic
- Use column masks in Unity Catalog for consistent masking
- Document sensitivity classifications
Row-Level Security:
- Implement with row filters in Unity Catalog
- Use dynamic views for complex filtering logic
- Consider performance impact of filter predicates

6. Full Practice Exam (60 Questions)

Now it’s time for the full practice exam, which simulates the actual certification test experience.

Instructions:

Time limit: 120 minutes
60 multiple-choice questions
All sections of the exam are covered proportionally
Select the best answer for each question

I’ll provide you with the first 10 questions to give you a sense of the format and difficulty level. In a real setting, you would continue with all 60 questions.

Question 1: A data engineer needs to track all changes (inserts, updates, and deletes) made to a Delta table for audit purposes. Which feature should they enable?

A) Delta Lake versioning
B) Change Data Feed
C) Transaction log retention
D) Time travel

Question 2: Which Delta Lake operation would most efficiently handle a scenario where millions of new records need to be inserted while a few thousand existing records need to be updated based on a matching key?

A) MERGE INTO
B) INSERT OVERWRITE
C) COPY INTO with deduplication
D) DELETE followed by INSERT

Question 3: A streaming application is processing sensor data and computing aggregations with a 10-minute window. The team notices that some events arrive up to 15 minutes late but are being dropped. Which change would allow these late events to be included in the aggregations?

A) Increase the trigger interval to 15 minutes
B) Change the watermark delay to 20 minutes
C) Switch to complete output mode
D) Use a tumbling window instead of a sliding window

Question 4: A data engineer observes that a Spark job with 1000 tasks has 990 tasks completing in approximately 2 minutes, while 10 tasks take over 30 minutes. What is the most likely cause?

A) Insufficient executor memory
B) Inefficient UDF implementation
C) Data skew
D) Incorrect partition count

Question 5: Which approach would be most effective for optimizing a Delta table that is frequently queried with filters on both timestamp (range queries) and product_category (equality filters on 50 distinct values)?

A) Partition by date extracted from timestamp and Z-ORDER by product_category
B) Partition by product_category and Z-ORDER by timestamp
C) Partition by both date and product_category
D) Z-ORDER by both timestamp and product_category without partitioning

Question 6: A data engineering team needs to implement a Type 2 SCD for customer dimension data. Which columns would be necessary to add to the dimension table?

A) valid_from_date, valid_to_date, is_current
B) updated_timestamp, updated_by
C) version_number, source_system
D) hash_key, change_type

Question 7: Which storage level would be most appropriate for a large DataFrame that is reused in multiple operations but doesn’t fit entirely in memory?

A) MEMORY_ONLY
B) MEMORY_ONLY_SER
C) MEMORY_AND_DISK
D) DISK_ONLY

Question 8: In a multi-task Databricks job, what is the best way to pass data between tasks without writing intermediate tables?

A) Use broadcast variables
B) Use dbutils.jobs.taskValues API
C) Store values in widget parameters
D) Use dbutils.notebook.run with arguments

Question 9: A data engineer needs to create a dynamic view that shows different columns to different user groups. Which approach is correct?

A) Use CASE expressions based on current_user() or is_member() functions
B) Create multiple views with different permissions
C) Use row-level security with column masking
D) Implement a Python UDF that returns different columns based on the user

Question 10: Which Ganglia metric would best indicate that a cluster is experiencing memory pressure?

A) High CPU utilization
B) Increased network traffic
C) Elevated disk I/O
D) High swap usage

Question 11: What is the most efficient way to apply changes from a CDC source to a Delta table when the CDC data contains operation types (insert, update, delete) and sequence numbers?

A) Write custom merge logic for each operation type
B) Use APPLY CHANGES INTO with appropriate key and sequence columns
C) Use individual INSERT, UPDATE, and DELETE statements based on operation type
D) Use DLT’s CDC capabilities with SYNC table directive

Question 12: Which command would you use to verify if the size of a Delta table is growing excessively due to retention of old file versions?

A) DESCRIBE HISTORY delta_table
B) DESCRIBE DETAIL delta_table
C) VACUUM delta_table DRY RUN
D) OPTIMIZE delta_table

Question 13: A data engineer needs to capture schema changes in source data while loading it into a Delta table. Which approach is most appropriate?

A) Use schema enforcement to reject incompatible records
B) Use schema evolution with mergeSchema option
C) Manually update the table schema before loading new data
D) Create a new table for each schema version

Question 14: When using Delta Live Tables, which expectation mode should be used if data quality is critical and any violation should stop the pipeline?

A) expect_all
B) expect_or_drop
C) expect_or_fail
D) expect_or_warn

Question 15: Which Structured Streaming output mode would you use for an aggregation query where you only want to see updated aggregation results in the output?

A) Append
B) Update
C) Complete
D) Upsert

Question 16: A team is experiencing slow performance with complex SQL queries involving multiple joins. Which Spark UI tab would provide the most valuable information for optimizing these queries?

A) Jobs
B) Stages
C) Storage
D) SQL

Question 17: Which technique would most effectively handle a streaming join where one stream has events that arrive significantly later than the other stream?

A) Increase the watermark delay on both streams
B) Use a left outer join with the delayed stream on the right side
C) Implement a stateful foreachBatch function
D) Convert the delayed stream to a static DataFrame

Question 18: What is the primary benefit of shallow cloning a Delta table compared to deep cloning?

A) Reduced storage requirements
B) Faster query performance
C) Support for more concurrent readers
D) Better compatibility with external systems

Question 19: Which operation in Delta Lake is designed specifically for handling small file issues?

A) VACUUM
B) OPTIMIZE
C) COMPACT
D) REWRITE

Question 20: Which approach provides the best security for accessing secrets in a Databricks notebook?

A) Store secrets as environment variables
B) Store secrets in Databricks Secret Scope
C) Hardcode secrets but restrict notebook permissions
D) Pass secrets as notebook parameters

Question 21: When implementing streaming aggregations with event-time windows, how do you ensure state doesn’t grow unbounded?

A) Use stateless transformations only
B) Set a watermark on the event time column
C) Use complete output mode
D) Increase trigger interval

Question 22: Which parameter in a Databricks job configuration controls automatic termination of a cluster after completion?

A) terminate_after_execution
B) auto_terminate
C) terminate_on_success
D) cluster_termination

Question 23: What is the recommended approach to handle schema drift in a data pipeline that processes JSON data with evolving structure?

A) Set strict schema validation to prevent inconsistent data
B) Use schema inference with schema evolution enabled
C) Create a fixed schema with all possible fields
D) Process raw data as strings and parse in the application layer

Question 24: Which Delta Lake property would you modify to extend the retention period for historical versions beyond the default 30 days?

A) delta.logRetentionDuration
B) delta.retentionDurationCheck
C) delta.deletedFileRetentionDuration
D) delta.enableVersionTracking

Question 25: In a multi-hop architecture, which layer should contain business-level aggregations optimized for specific analytics use cases?

A) Bronze layer
B) Silver layer
C) Gold layer
D) Platinum layer

Question 26: Which join strategy would Spark choose when joining a very large table with a small table that has been broadcasted?

A) Broadcast hash join
B) Shuffle hash join
C) Sort-merge join
D) Nested loop join

Question 27: What is the primary benefit of using Auto Loader in Databricks compared to manually tracking and loading new files?

A) Higher throughput for batch processing
B) Support for more file formats
C) Automatic schema inference and evolution
D) Efficient incremental processing without file listings

Question 28: Which REST API endpoint would you use to retrieve the execution results of a completed Databricks job run?

A) /api/2.0/jobs/runs/get-output
B) /api/2.0/jobs/runs/get-result
C) /api/2.0/jobs/runs/export
D) /api/2.0/jobs/runs/result

Question 29: What does the Z in Z-ORDER represent in the context of Delta Lake?

A) Zero-latency indexing
B) Z-curve spatial filling indexing
C) Zig-zag processing pattern
D) Zone-based file organization

Question 30: Which mechanism in Delta Lake helps prevent concurrent writes from causing data corruption?

A) Write-ahead logging
B) Journaling
C) Optimistic concurrency control
D) Pessimistic locking

Question 31: A data pipeline needs to ensure that customer records always have valid email addresses. Which is the most efficient approach in Delta Live Tables?

A) Implement a WHERE clause in the SELECT statement
B) Use a CASE statement to replace invalid emails with NULL
C) Apply a constraint with expect_or_drop
D) Create a separate quality monitoring job

Question 32: Which storage format serves as the underlying file format for Delta Lake tables?

A) Avro
B) Parquet
C) ORC
D) JSON

Question 33: What is the key benefit of implementing table constraints in Delta Lake?

A) Improved query performance
B) Data quality enforcement
C) Reduced storage requirements
D) Simplified schema evolution

Question 34: In the context of Delta Lake, what does the VACUUM command do?

A) Optimizes table statistics for better query planning
B) Compacts small files into larger ones
C) Removes files no longer referenced by the table
D) Forces garbage collection of cached data

Question 35: Which feature would you use to efficiently propagate deletes from a source system to a target Delta table?

A) DELETE WHERE condition
B) MERGE with delete clause
C) Change Data Feed
D) APPLY CHANGES with sequence tracking

Question 36: What is the recommended approach for handling late-arriving data in a streaming application?

A) Increase the trigger interval
B) Configure a watermark with appropriate delay
C) Use foreachBatch to implement custom logic
D) Switch to complete output mode

Question 37: Which command allows you to update multiple columns in a Delta table based on matching a condition?

A) UPDATE
B) MERGE
C) ALTER
D) UPSERT

Question 38: What is the main advantage of using Unity Catalog for data governance?

A) Improved query performance
B) Enhanced security with fine-grained access controls
C) Reduced storage costs
D) Automatic data quality validation

Question 39: Which approach would you use to implement row-level security in a Delta table?

A) Create separate tables for each security level
B) Use dynamic views with filtering based on user attributes
C) Encrypt sensitive rows
D) Use column-level access controls

Question 40: When analyzing Spark performance issues, which metric indicates data skew most directly?

A) Task duration variance within a stage
B) Overall job completion time
C) Number of tasks per stage
D) Executor CPU utilization

Question 41: Which feature is most useful for implementing incremental ETL patterns with Delta Lake?

A) Time travel
B) Schema enforcement
C) COPY INTO
D) Change Data Feed

Question 42: In a Delta Live Tables pipeline, what does the LIVE keyword indicate?

A) The table is continuously updated with streaming data
B) The table is managed by DLT and follows lineage tracking
C) The table has constraints that are actively enforced
D) The table is exposed for querying while being updated

Question 43: Which approach is most efficient for updating a subset of columns in a large Delta table?

A) CREATE OR REPLACE TABLE
B) INSERT OVERWRITE
C) UPDATE SET
D) MERGE INTO

Question 44: What is the best practice for organizing Delta tables for optimal query performance?

A) Create many small tables with highly specific data
B) Partition by columns frequently used in WHERE clauses
C) Z-ORDER by all columns used in queries
D) Avoid partitioning to simplify file management

Question 45: Which method provides the most efficient way to access a small lookup table in a Spark join?

A) Cache the lookup table before joining
B) Broadcast the lookup table to all executors
C) Repartition both tables on the join key
D) Use a subquery instead of a join

Question 46: What is the primary purpose of the Databricks CLI?

A) To provide a SQL interface for querying data
B) To automate and script Databricks operations
C) To monitor cluster performance
D) To develop and test Spark code

Question 47: Which pattern is most appropriate for implementing a multi-environment CI/CD pipeline for Databricks?

A) Using separate workspaces for each environment
B) Using branches to represent different environments
C) Deploying all code to a single workspace with environment-specific parameters
D) Creating separate notebooks for each environment

Question 48: What is the recommended approach for passing credentials securely to a Databricks job?

A) Store credentials in environment variables
B) Use Databricks Secret Scopes
C) Include credentials in job parameters
D) Store credentials in DBFS with restricted permissions

Question 49: Which type of cluster is most appropriate for running scheduled production jobs?

A) All-purpose cluster
B) High Concurrency cluster
C) Single Node cluster
D) Job cluster

Question 50: What is the most efficient way to handle data quality issues in streaming data?

A) Apply filters before processing
B) Use watermarks to drop late data
C) Implement quality checks in foreachBatch
D) Configure expectations in Delta Live Tables

Question 51: Which approach provides the best performance for querying a subset of a very large Delta table partitioned by date?

A) Use a subquery to filter the partition first
B) Include the partition column in the WHERE clause
C) Cache the entire table before querying
D) Repartition the data before querying

Question 52: What happens when a constraint is added to an existing Delta table that contains records violating the constraint?

A) The constraint is added but not enforced retroactively
B) The constraint addition fails
C) Existing violating records are automatically deleted
D) The constraint is only applied to future write operations

Question 53: Which API would you use to programmatically manage Databricks jobs?

A) Compute API
B) Jobs API
C) Workflow API
D) Scheduler API

Question 54: What is the primary benefit of using partitioning in Delta tables?

A) Improved storage compression
B) Automatic data clustering
C) Efficient data pruning during queries
D) Simplified data management

Question 55: Which permission is required to view query history in Databricks SQL?

A) CAN VIEW
B) CAN RUN
C) CAN MANAGE
D) CAN MODIFY

Question 56: What feature in Delta Lake helps maintain table statistics for query optimization?

A) Data skipping
B) File statistics tracking
C) Bloom filters
D) Adaptive Query Execution

Question 57: Which approach is recommended for handling schema changes in a Delta table?

A) Create a new table with each schema change
B) Set mergeSchema = true when writing new data
C) Maintain schema versions in separate columns
D) Convert all columns to string type for flexibility

Question 58: What is the main purpose of time travel in Delta Lake?

A) To schedule future queries
B) To optimize query performance over time ranges
C) To query historical versions of a table
D) To track data access patterns over time

Question 59: Which command would you use to see the history of operations performed on a Delta table?

A) DESCRIBE HISTORY table_name
B) SHOW HISTORY table_name
C) SELECT * FROM HISTORY(table_name)
D) DESCRIBE DETAIL table_name

Question 60: What is the best approach for implementing a Type 2 SCD in Delta Lake?

A) Use MERGE with standard source-to-target matching
B) Use MERGE with conditional updates for current records and inserts for new versions
C) Use INSERT OVERWRITE with all records having new surrogate keys
D) Create a view that unions all historical versions

Answer Key (first 10 questions):

B - Change Data Feed tracks all row-level changes (inserts, updates, deletes) and is designed for audit and incremental processing scenarios.
A - MERGE INTO efficiently handles the combined scenario of inserting new records and updating existing ones in a single operation.
B - Increasing the watermark delay to 20 minutes would allow events arriving up to 15 minutes late to be included in the aggregations.
C - The pattern of a small number of tasks taking significantly longer than others is a classic indicator of data skew.
A - For this query pattern, partitioning by date (extracted from timestamp) provides efficient pruning for range queries, while Z-ORDER by product_category optimizes the equality filters within partitions.
A - A Type 2 SCD requires tracking the validity period (valid_from_date, valid_to_date) and current status (is_current) for each version of a dimension record.
C - MEMORY_AND_DISK is appropriate for large DataFrames that don’t fit entirely in memory, as it will store as much as possible in memory and spill the rest to disk.
B - The dbutils.jobs.taskValues API is specifically designed to pass values between tasks in a multi-task job.
A - Dynamic views implement column-level security using CASE expressions with current_user() or is_member() functions to return different values based on the user’s identity or role membership.
D - High swap usage indicates that the system is using disk space as virtual memory because physical memory is exhausted, which is a clear sign of memory pressure.
B - APPLY CHANGES INTO is specifically designed for CDC processing, automatically handling inserts, updates, and deletes based on operation type and sequence.
C - VACUUM with DRY RUN shows which files would be deleted based on the current retention period, helping identify if excessive versions are being retained.
B - Using schema evolution with mergeSchema option allows the Delta table to automatically adapt to changes in the source schema.
C - expect_or_fail in DLT causes the entire pipeline to fail if any records violate the constraint, appropriate when data quality is critical.
B - Update mode outputs only the rows that have been updated since the last trigger, making it ideal for updated aggregation results.
D - The SQL tab provides detailed information about query execution plans and optimization opportunities specific to SQL queries.
A - Increasing the watermark delay on the stream with late-arriving events ensures these events can still be processed within the join.
A - Shallow clones reference the original data files (reducing storage requirements) while maintaining an independent transaction log.
B - OPTIMIZE is specifically designed to address small file issues by compacting them into larger, more efficiently sized files.
B - Databricks Secret Scope provides secure storage and retrieval of secrets without exposing them in code or logs.
B - Setting a watermark on the event time column allows the system to discard state for windows that are no longer needed, preventing unbounded growth.
A - The terminate_after_execution parameter controls whether a cluster automatically terminates after job completion.
B - Using schema inference with schema evolution enabled allows the pipeline to adapt to changes in the JSON structure over time.
A - delta.logRetentionDuration controls how long historical versions of a Delta table are retained.
C - The Gold layer in a multi-hop architecture contains business-level aggregations and metrics optimized for specific analytics use cases.
A - When joining with a broadcasted small table, Spark will choose a broadcast hash join, which eliminates the need for shuffling.
D - Auto Loader efficiently processes new files incrementally without expensive file listing operations, which is its primary benefit.
A - The /api/2.0/jobs/runs/get-output endpoint retrieves the execution results of a completed job run.
B - Z in Z-ORDER refers to Z-curve spatial filling indexing, which maps multidimensional data to one dimension while preserving locality.
C - Optimistic concurrency control prevents data corruption from concurrent writes by detecting conflicts during the commit phase.
C - Using expect_or_drop in DLT applies a constraint that automatically removes records with invalid email addresses during processing.
B - Parquet is the underlying file format used by Delta Lake tables, providing columnar storage and efficient compression.
B - Table constraints in Delta Lake enforce data quality rules, preventing invalid data from being written to the table.
C - VACUUM removes files that are no longer referenced by the table and are older than the retention threshold.
D - APPLY CHANGES with sequence tracking efficiently propagates deletes from a source system to a target Delta table.
B - Configuring an appropriate watermark delay is the recommended approach for handling late-arriving data in streaming applications.
A - The UPDATE command allows updating multiple columns based on a condition in a Delta table.
B - Unity Catalog’s main advantage is enhanced security with fine-grained access controls across workspaces and cloud providers.
B - Dynamic views with filtering based on user attributes is the recommended approach for implementing row-level security.
A - Task duration variance within a stage directly indicates data skew, where some tasks process significantly more data than others.
D - Change Data Feed is most useful for implementing incremental ETL patterns as it tracks row-level changes efficiently.
B - The LIVE keyword in DLT indicates that the table is managed by Delta Live Tables and participates in lineage tracking.
C - UPDATE SET is most efficient for updating a subset of columns in a large Delta table as it only modifies the specified columns.
B - Partitioning by columns frequently used in WHERE clauses provides the best query performance through partition pruning.
B - Broadcasting a small lookup table to all executors is the most efficient method for using it in Spark joins.
B - The primary purpose of the Databricks CLI is to automate and script Databricks operations programmatically.
A - Using separate workspaces for each environment (dev, test, prod) is the most appropriate pattern for implementing a multi-environment CI/CD pipeline.
B - Databricks Secret Scopes provide a secure way to manage and access credentials in jobs without exposing them.
D - Job clusters are specifically designed for running scheduled production jobs, automatically starting and terminating as needed.
D - Configuring expectations in Delta Live Tables provides the most efficient way to handle data quality issues in streaming data.
B - Including the partition column in the WHERE clause enables partition pruning, which significantly improves query performance.
A - When adding a constraint to an existing Delta table, it is added but not enforced retroactively on existing data.
B - The Jobs API is used to programmatically manage Databricks jobs, including creation, modification, and execution.
C - The primary benefit of partitioning is efficient data pruning during queries, as irrelevant partitions can be skipped entirely.
C - CAN MANAGE permission is required to view query history in Databricks SQL.
A - Data skipping helps maintain statistics about min/max values in files, enabling the query optimizer to skip irrelevant files.
B - Setting mergeSchema = true when writing new data is the recommended approach for handling schema changes in Delta tables.
C - The main purpose of time travel in Delta Lake is to query historical versions of a table at specific points in time.
A - DESCRIBE HISTORY table_name shows the history of operations performed on a Delta table.
B - For Type 2 SCDs, using MERGE with conditional logic to update current records (setting end dates and is_current flags) and insert new versions is the best approach.

7. Final Preparation Strategies

Based on your performance on the practice exams, here are some final preparation strategies for success:

Focus on Areas of Uncertainty:

Review concepts that you found challenging in the practice exams
Create flashcards for technical definitions and key differences
Practice explaining complex concepts in simple terms

Practical Application:

Connect theoretical concepts to practical scenarios
Think through real-world implementations of exam topics
Consider how different components interact in end-to-end solutions

Last-Minute Review:

Scan through key diagrams and decision trees
Review the core functionality of each Delta Lake operation
Revisit common patterns for streaming and batch processing
Refresh your memory on optimization strategies

Day-Before Preparation:

Get a good night’s sleep (crucial for cognitive performance)
Review your notes but don’t try to learn new material
Practice relaxation and focus techniques
Prepare everything you need for the exam day

During the Exam:

Read questions carefully before jumping to answers
Use the process of elimination for challenging questions
Pace yourself with periodic time checks
Answer all questions, even if uncertain

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Table of contents

Preparation Plan

Fundamentals & Databricks Tooling

Data Processing

Data Modeling & Security

Monitoring, Testing & Deployment

Knowledge Base

Fundamentals & Databricks Tooling

Introduction to Databricks Platform and Delta Lake Fundamentals

1. Introduction to Databricks Platform Architecture

2. Delta Lake Fundamentals and Transaction Log Concepts

3. ACID Properties in Delta Lake

4. Hands-on: Creating and Optimizing Delta Tables

5. Delta Lake Optimization Techniques

6. Z-Ordering and Advanced Indexing

7. Delta Clone Operations and Use Cases

8. Bloom Filters and File Size Optimization

Quiz: Databricks Tooling Fundamentals

Key Takeaways

Data Processing

1. Batch Processing Fundamentals

2. Spark Partitioning Strategies and Optimization

3. Partition Hints: Coalesce, Repartition, Rebalance

4. Hands-on: Optimizing Batch Workloads

5. Incremental Processing with Structured Streaming

6. Stream-Static Joins Implementation

7. Change Data Feed (CDF) and Incremental Data Processing

8. Quiz: Batch and Streaming Processing Techniques

Key Takeaways

Data Modeling & Security

1. Lakehouse Medallion Architecture

2. Data Quality Enforcement

3. Implementing Slowly Changing Dimensions (SCDs)

4. Hands-on: Building a Medallion Architecture Pipeline

5. Security & Governance Fundamentals

6. Dynamic Views for Data Masking

7. Row and Column-Level Security

8. Quiz: Data Modeling and Security Concepts

Key Takeaways

Monitoring, Testing & Deployment

1. Spark UI Performance Analysis

2. Monitoring Streaming Jobs and SLAs

3. Ganglia UI and Cluster UI Interpretation

4. Hands-on: Debugging Performance Issues

5. Databricks Jobs Configuration and Dependencies

6. Databricks CLI and REST API Implementation

7. CI/CD Patterns for Databricks

8. Quiz: Monitoring and Deployment Concepts

Key Takeaways

Comprehensive Review & Final Assessment

1. Comprehensive Review of Key Exam Topics

2. Half-Length Practice Exam (30 Questions)

3. Review of Practice Exam Results

4. Exam Strategy Discussion

5. Final Concept Clarification

6. Full Practice Exam (60 Questions)

7. Final Preparation Strategies