Intensive Databricks Data Engineering Immersive Training Course

Week 1 — Introduction to Data Engineering

• Data engineering fundamentals and modern data stacks
• Data ingestion patterns and sources
• Batch vs streaming concepts and use cases
• Hands-on lab: ingesting sample data into cloud storage

Week 2 — Databricks Lakehouse Foundation Badge

• Databricks platform fundamentals and workspace navigation
• Delta Lake concepts: ACID, time travel, and schema evolution
• Workspace security, access controls, and Unity Catalog basics
• Hands-on lab: Delta table creation and management

Week 3 — Advanced SQL on Databricks

• Advanced SQL constructs and window functions at scale
• Query optimization, explain plans, and cost-aware patterns
• Materialized views, caching, and performance tuning
• Hands-on lab: optimizing analytical queries on large datasets

Week 4 — Databricks Certified Developer for Apache Spark (Prep)

• Spark architecture, RDDs, DataFrames, and Datasets deep dive
• Key Spark transformations and actions; performance considerations
• Spark streaming basics and structured streaming patterns
• Practice exam exercises and hands-on test problems

Week 5 — Introduction to Data Modeling

• Concepts: dimensional modeling, star/schema design, and normalization
• Lakehouse modeling vs traditional warehouse approaches
• Design patterns for analytics-ready datasets
• Hands-on lab: building consumption-ready tables and views

Week 6 — Introduction to Import Tools & Data Ingestion Automation

• Connectors and ingestion tools for Databricks (AWS Glue, Data Factory, Kafka)
• Stream ingestion patterns and micro-batch designs
• Data validation, quality checks, and schema enforcement
• Hands-on lab: building resilient ingestion pipelines

Week 7 — Introduction to Git Flow and CI/CD for Data Engineering

• Git Flow branching strategies and repository organization
• CI/CD pipelines for notebooks, jobs, and infrastructure as code
• Testing, linting, and deployment automation for data code
• Hands-on lab: implement Git-based workflow and automated job deployment

Week 8 — Databricks Certified Data Engineer Associate (Prep) & Data Engineering Patterns

• Certification topics review and practical exercises
• Architectural patterns: bronze/silver/gold, CDC, slowly changing dimensions
• Operational patterns: monitoring, alerting, and lineage
• Hands-on lab: end-to-end pipeline applying engineering patterns

Week 9 — Introduction to Airflow and Astronomer; Scripting

• Airflow concepts: DAGs, tasks, operators, and scheduling
• Astronomer platform overview and orchestration best practices
• Scripting for automation: Python scripting patterns for data tasks
• Hands-on lab: orchestrate Databricks jobs with Airflow DAGs

Week 10 — Data Visualization, Tableau, and Customized Final Project

• Connecting Tableau to Databricks and best practices for BI layers
• Dashboard design principles and performance-aware visualizations
• Capstone: customized final project scoping, implementation, and presentation
• Final presentations, peer review, and instructor feedback

Summary and Next Steps