Databricks Lakehouse & Spark Data Engineering Bootcamp Certificate

Checkpoint 1: Create a free Databricks workspace; map control/data plane, catalogs/schemas/volumes and establish a governed bronze/silver/gold namespace.
Checkpoint 2: Ingest raw files with PySpark and Auto Loader; enforce explicit schema, checkpoints and idempotent/replayable processing.
Checkpoint 3: Build Delta tables with merge/upsert, schema evolution, time travel and a documented recovery/rollback test.
Checkpoint 4: Create a declarative batch + streaming pipeline with expectations/data-quality checks and one CDC or slowly changing dimension flow.
Checkpoint 5: Apply Unity Catalog permissions, lineage and least-privilege access; prove discoverability for a downstream BI/ML consumer.
Checkpoint 6: Profile a slow workload; improve joins/partitioning/AQE/caching or Delta layout and quantify latency, compute and cost change.

Day 1: Define a real data domain, contracts, SLAs, quality rules, governance boundaries and bronze/silver/gold target architecture.
Day 2: Build batch ingestion plus one incremental/streaming source using Auto Loader or Structured Streaming with checkpointing.
Day 3: Transform with PySpark/Spark SQL; publish Delta tables with schema enforcement/evolution and merge-based change handling.
Day 4: Orchestrate a production pipeline with expectations, lineage, retries and a controlled bad-data/failure scenario.
Day 5: Govern catalogs/schemas/tables with least privilege, auditability and one downstream analytics or ML consumer.
Day 6: Tune one expensive pipeline using explain plans, partitioning/joins/AQE or Delta optimization; capture before/after evidence.
Day 7: Publish repo/notebooks, architecture, data contract, lineage screenshot, quality report, performance/cost evidence, demo and runbook.