Delta Lake

Course Details
Code: DB200
Tuition (USD): $1,500.00 • Classroom (1 day)
$1,500.00 • Virtual (1 day)

This 1-day course is for data engineers, architects, data scientists and software engineers who want to use Databricks Delta for ETL processing on Data Lakes. The course ends with a capstone project building a complete data pipeline using Databricks Delta.

Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. Students may keep the notebooks and continue to use them with the free Databricks Community Edition offering after the class ends; all examples are guaranteed to run in that environment.

Skills Gained

After taking this class, students will be able to:

  • Use the interactive Databricks notebook environment.
  • Use Databricks Delta to create, append and upsert data into a Data Lake.
  • Use Databricks Delta to manage and extract actionable insights out of a Data Lake.
  • Use Databricks Delta’s advanced optimization features to speed up queries.
  • Use Databricks Delta to seamlessly ingest streaming and historical data.
  • Implement a Databricks Delta data pipeline architecture

Who Can Benefit

Data engineers, software engineers, dev-ops, IT operations, and team-leads with experience using Databricks.


Completed the Getting Started with Apache Spark™ SQL, Getting Started with Apache Spark™ DataFrames, or ETL Part 1 course, or already have similar knowledge

Course Details


Supported platforms include Azure Databricks, Databricks Community Edition, and non-Azure Databricks.

  • If you’re planning to use the course on Azure Databricks, select the “Azure Databricks” Platform option.
  • If you’re planning to use the course on Databricks Community Edition or on a non-Azure version of Databricks, select the “Other Databricks” Platform option.

Lab Requirements

  • A computer or laptop
  • Chrome or Firefox Web Browser Internet explorer and Safari are not supported
  • Internet access with unfettered connections to the following domains:
  • 1. * - required
  • 2. * - highly recommended
  • 3. - required
  • 4. - helpful but not required
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