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Reference: https://www.databricks.com/learn/certification/data-engineer-professional
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modelling | 6% | - Delta Lake table design - Schema design and management - Medallion Architecture implementation |
| Topic 2: Developing Code for Data Processing using Python and SQL | 22% | - Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools |
| Topic 3: Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Compliance standards implementation - Access control and permissions |
| Topic 4: Data Ingestion & Acquisition | 7% | - Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution |
| Topic 5: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 6: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices |
| Topic 7: Cost & Performance Optimisation | 13% | - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching |
| Topic 8: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Topic 9: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Pipeline observability and logging - Performance and health monitoring |
| Topic 10: Data Governance | 7% | - Unity Catalog management - Data lineage and metadata tracking - Policy enforcement |
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