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Day | Duration | Module | Topics Covered |
---|---|---|---|
Day 1 | 4 Hours | Introduction & Setup | Databricks overview, Lakehouse architecture, Workspace setup, Cluster creation, DBFS |
Day 2 | 4 Hours | Notebooks & Data Ingestion | Python/SQL Notebooks, Visualizations, Mount ADLS, Read from Azure SQL/Blob |
Day 3 | 4 Hours | Delta Lake & Table Management | ACID, Schema enforcement, MERGE, OPTIMIZE, Bronze/Silver/Gold strategy |
Day 4 | 4 Hours | Data Engineering with Spark | Joins, Aggregations, Window functions, Partitioning, Bad record handling |
Day 5 | 4 Hours | Streaming & Auto Loader | Structured Streaming, Auto Loader, Watermarking, Checkpointing |
Day 6 | 4 Hours | Unity Catalog & Security | Managed/external tables, Access control, Lineage, Token management |
Day 7 | 4 Hours | BI, SQL & Databricks SQL | Databricks SQL, Dashboards, Power BI integration, Delta Live Tables |
Day 8 | 4 Hours | Data Governance & Quality in Databricks | Data expectations, Constraints, Expectations in DLT, Unity Catalog auditing, Alerting & Monitoring |
Day 9 | 4 Hours | Performance Tuning & Optimization | Caching, Broadcast joins, Z-Ordering, File sizing, Cluster config tuning, Query optimization |
Day 10 | 4 Hours | Real-time Retail Analytics Project | Streaming ingestion, Bronze-Silver-Gold pipeline, Fact/Dim tables, BI dashboard |
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