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Data EngineeringIntermediate → SeniorNEW
Databricks — Delta Lake & PySpark
Build production data pipelines on Databricks — Delta Lake ACID transactions and MERGE upserts, Medallion Architecture, Delta Live Tables, Auto Loader, Structured Streaming, advanced PySpark optimizations, MLflow model lifecycle, Unity Catalog governance, and Feature Store.
4.9rating1,020 students6h 30m total4 lessons
What you'll learn
Create Delta tables with ACID transactions, time travel, schema enforcement, and MERGE upserts
Build Medallion Architecture pipelines: Bronze append → Silver MERGE → Gold aggregation
Use Delta Live Tables with @dlt.table and @dlt.expect for declarative quality pipelines
Ingest files incrementally with Auto Loader and stream Kafka into Delta tables
Use dbutils for secrets, file operations, and parameterized notebook workflows
Optimize PySpark with Photon, Liquid Clustering, AQE, broadcast joins, and salting
Track ML experiments with MLflow and register models with Unity Catalog
Govern data with Unity Catalog: column masks, row filters, Delta Sharing, and lineage
Final Project
Build a full Databricks data platform: Auto Loader → DLT Bronze/Silver/Gold pipeline → MLflow model tracking → Unity Catalog governance
Curriculum
4 lessons · 6h 30mCourse Info
Lessons4 lessons
Total time6h 30m
LevelIntermediate → Senior
Students1,020
Rating4.9 / 5.0