Course Outline
Databricks Platform and Lakehouse Fundamentals
- Databricks Lakehouse architecture and components.
- Organizing workspaces and catalogs.
Databricks Workspace and Notebooks
- Workspace navigation and notebook-based development.
- Structuring code into reusable notebooks.
Apache Spark Architecture and Execution
- Spark runtime architecture and execution model.
- Lazy evaluation and the job DAG.
PySpark DataFrames and the DataFrame API
- DataFrame abstractions and schemas.
- Core DataFrame operations and column expressions.
Translating SQL to PySpark DataFrames
- Translating core SQL clauses to DataFrame operations.
- Window functions and aggregations in PySpark.
Reading and Writing Data in Databricks
- Reading from common file and database sources.
- Writing and partitioning data in the Lakehouse.
Delta Lake and Table Management
- Delta tables and ACID transactions.
- Time travel and schema evolution.
Data Cleaning and Transformation Patterns
- Data cleaning and type conversion.
- Building reusable transformation logic.
User-Defined Functions and Modular Code
- Python UDFs and pandas UDFs.
- Modularizing procedural logic into functions.
Performance Tuning and Optimization
- Partitioning and caching strategies.
- Diagnosing bottlenecks with the Spark UI.
Structured Streaming Fundamentals
- Batch versus streaming processing models.
- Streaming DataFrames and basic aggregations.
Databricks Jobs and Workflow Orchestration
- Scheduling notebooks as jobs and tasks.
- Building multi-step workflows with dependencies.
Unity Catalog and Data Governance
- Unity Catalog architecture and namespaces.
- Access control and data lineage.
Testing, Debugging, and Production Practices
- Unit testing PySpark logic.
- Debugging and code quality standards.
End-to-End Financial Services Use Cases
- Building an end-to-end banking ETL pipeline.
- Translating legacy SQL processes to PySpark.
Migrating SQL Workloads to PySpark
- Migration strategy and planning patterns.
- Incremental conversion of SQL workflows to PySpark.
Requirements
- Experience with Python programming, including functions and data types.
- Understanding of SQL, covering joins, aggregations, and subqueries.
- No prior experience with Databricks or PySpark is required.
Audience
- Data engineers, data analysts, and data professionals.
- Teams transitioning existing SQL-based workflows to Databricks and PySpark.
Testimonials (1)
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
