Databricks

Section 1: Introduction to Databricks and Data Engineering

Objective:Understand the Databricks platform and its role in data engineering.

Duration:4 hours

Topics:

• Overview of data engineering: Roles, responsibilities, and modern tools
• Introduction to Databricks: Lakehouse architecture, workspaces, and clusters
• Databricks ecosystem: Notebooks, Delta Lake, MLflow, and Databricks SQL
• Setting up a Databricks environment: Community Edition or enterprise workspace
• Best practices: Cluster management, cost optimization, and security (RBAC, secret scopes)

Hands-On Lab:

• Create a Databricks workspace and configure a cluster with auto-scaling.
• Set up a secret scope for secure credential management.
• Run a sample PySpark notebook to explore the Databricks UI.

Section 2: PySpark Fundamentals in Databricks

Objective:Master PySpark for data processing and transformation.

Duration:6 hours

Topics:

• PySpark overview: SparkSession, DataFrames, and lazy evaluation
• Core DataFrame operations: Filtering, grouping, joining, and aggregations
• RDDs vs. DataFrames: Use cases and performance considerations
• Data I/O: Reading/writing CSV, Parquet, JSON, and Delta formats
• Spark SQL: Creating views, querying with SQL, and UDFs
• Schema management: Defining, inferring, and enforcing schemas

Hands-On Lab:

• Read a JSON dataset into a DataFrame with a defined schema.
• Perform transformations (e.g., filter, groupBy, join) on a retail dataset.
• Create a temporary view and query it with Spark SQL.

Project:

• Process a customer dataset in PySpark, generating a summary report of sales by region.

Section 3: Delta Lake for Scalable Data Lakes

Objective:Implement Delta Lake for reliable data management.

Duration:6 hours

Topics:

• Delta Lake fundamentals: ACID transactions, schema enforcement, and storage
• Creating Delta tables: Write, update, and delete operations
• Advanced features: Time travel, versioning, and Z-order indexing
• Upserts and merges: Incremental updates and SCD Type 2
• Data quality: Assert Transformations and constraints
• Optimizing Delta tables: Compaction, vacuuming, and partitioning

Hands-On Lab:

• Create a Delta table from a Parquet dataset.
• Perform a merge operation to upsert new transaction data.
• Use time travel to query a previous version and vacuum old files.

Project:

• Build a Delta Lake pipeline to ingest and update a product inventory dataset, enforcing data quality with constraints.

Section 4: Delta Live Tables for Simplified ETL

Objective:Streamline ETL pipelines with Delta Live Tables.

Duration:6 hours

Topics:

• Delta Live Tables (DLT) overview: Declarative ETL and continuous processing
• Defining DLT pipelines: Python and SQL syntax
• Pipeline modes: Batch and streaming
• Data quality: Expectations and quarantine rules
• Monitoring DLT: Pipeline status, lineage, and alerts
• Integration with storage systems: File systems, cloud storage, and databases

Hands-On Lab:

• Create a DLT pipeline to process streaming log data from a file source.
• Apply expectations to filter invalid records and quarantine them.
• Monitor the pipeline and visualize lineage in the DLT UI.

Project:

• Develop a DLT pipeline to process real-time clickstream data, storing curated results in Delta Lake.

Section 5: Spark Performance Optimization

Objective:Optimize Spark jobs for performance and scalability.

Duration:8 hours

Topics:

• Spark execution model: Stages, tasks, shuffles, and DAGs
• Caching and persistence: Memory vs. disk, when to cache
• Partition management: Repartition, coalesce, and optimal partition sizing
• Data skew mitigation: Salting (e.g., floor(rand() * salt_factor).cast(“string”))
• Static and dynamic pruning: Partition pruning and predicate pushdown
• Adaptive Query Execution (AQE): Dynamic coalescing and skew handling

Hands-On Lab:

• Optimize a skewed join on a sales dataset using salting.
• Apply static pruning by partitioning data by FiscalYear and filtering.
• Enable AQE and measure performance improvements with caching.

Project:

• Optimize a PySpark job processing a large dataset (e.g., 50GB), applying salting, partitioning, and AQE, and document performance gains.

Section 6: Advanced Databricks Features and MLflow

Objective:Leverage advanced Databricks tools for analytics and ML.

Duration:6 hours

Topics:

• Medallion architecture: Bronze, Silver, Gold layers for structured pipelines
• Structured Streaming: Real-time processing with Delta Lake
• MLflow: Tracking experiments, models, and runs
• Databricks SQL: Building dashboards and analytics queries
• Unity Catalog: Data governance, lineage, and access control
• Collaborative workflows: Notebook sharing, comments, and version control

Hands-On Lab:

• Build a streaming pipeline using Structured Streaming to process event data.
• Use MLflow to track a simple regression model experiment.
• Create a Databricks SQL dashboard for a sales dataset.

Project:

• Implement a medallion-based streaming pipeline for IoT sensor data, tracking experiments with MLflow and visualizing results in Databricks SQL.

Section 7: Pipeline Orchestration and Monitoring

Objective:Orchestrate and monitor Databricks workflows.

Duration:6 hours

Topics:

• Databricks Workflows: Scheduling and managing jobs
• Error handling: Retries, alerts, and failover strategies
• Monitoring: Job logs, cluster metrics, and alerting
• Monitoring: Job logs, cluster metrics, and alerting
• Version control: Integrating notebooks with Git
• Best practices: Modularity, Data Vault modeling, and logging

Hands-On Lab:

• Create a Databricks Workflow to schedule a multi-step pipeline.
• Set up email alerts for job failures and monitor cluster utilization.
• Commit a notebook to a Git repository for version control.

Project:

• Build an orchestrated pipeline using Databricks Workflows, applying Data Vault modeling, and monitor performance with custom alerts.

Section 8: Capstone Project and Career Preparation

Objective:Apply skills to a real-world project and prepare for data engineering roles.

Duration:6 hours

Topics:

• Review of key concepts: PySpark, Delta Lake, DLT, and optimizations
• Building a portfolio-worthy project with medallion architecture
• Career preparation: Resume building with Apache Spark expertise, LinkedIn optimization, and interview tips
• Common interview questions: PySpark coding (e.g., DataFrame operations, skew mitigation), Delta Lake, and pipeline design
• Certification paths: Databricks Certified Data Engineer Associate

Capstone Project:

• Design and implement a complete data pipeline for a fictional logistics company:
  • Ingest raw data into a file system or cloud storage.
  • Transform data using PySpark, Delta Lake, and Delta Live Tables, applying optimizations like salting and static pruning..
  • Store curated data in Delta tables.
  • Build a Databricks SQL dashboard for analytics.
  • Document the architecture with Data Vault principles and present findings in a notebook.

Career Prep:

• Mock interview with PySpark coding (e.g., optimizing joins, handling skew) and Databricks architecture questions.
• Create a GitHub repository with project code, notebooks, and pipeline documentation, versioned using Git.

Additional Resources

• Tools Covered: Databricks, PySpark, Delta Lake, Delta Live Tables, MLflow, Databricks SQL, Unity Catalog, Databricks Workflows
• Sample Datasets: Retail sales, logistics data, IoT streams, and clickstream logs
• Supplementary Materials: Cheat sheets for PySpark, Delta Lake, and Spark optimizations; access to recorded sessions
• Community Support: Slack/Discord channel for peer collaboration and instructor Q&A

Assessment and Certification

• Quizzes: Weekly quizzes to reinforce concepts
• Labs and Projects: Graded hands-on assignments
• Capstone Evaluation: Assessed on pipeline functionality, optimization, and presentation
• Certificate: Awarded upon successful completion of the course and capstone project