Managed Delta Lake (with capstone)


This hands-on self-paced training course targets Data Engineers, Data Scientists and Data Analysts who want to use Managed Delta Lake for ETL processing on data lakes. The course ends with a capstone project building a complete data pipeline using Managed Delta Lake.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.



3-6 hours, 75% hands-on practical experiences

Format: Self-paced eLearning

The course is a series of seven self-paced lessons plus a final capstone project building a complete data pipeline using Managed Delta Lake. Each lesson includes hands-on exercises.


This course is intended to be run in a Databricks workspace. The course contains Databricks notebooks for both Azure Databricks and AWS Databricks; you can run the course on either platform.

Note: Access to a Databricks workspace is not part of your course purchase price. You are responsible for getting access to Databricks. See the FAQ for instructions on how to get access to an Databricks workspace.

Learning Objectives

During this course learners

  • Use the interactive Databricks notebook environment.
  • Create, append and upsert data into a data lake.
  • Use Managed Delta Lake to manage and extract actionable insights out of a data lake.
  • Use Databricks advanced optimization features to speed up queries.
  • Seamlessly ingest streaming and historical data.
  • Implement a data pipeline using Managed Delta Lake.


  1. Introducing Delta Lake
  2. Create
  3. Append
  4. Upsert
  5. Streaming
  6. Architecture
  7. Capstone Project

Target Audience

  • Primary Audience: Data Engineers


Lab Requirements


6 hours