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Performance Optimization with Delta Lake, Apache Spark, and Photon

In this course, you’ll learn how to optimize workloads and physical data layouts with Spark and Delta Lake as well as analyze the Spark UI to assess performance and debug applications.

Skill Level



  • Designing the Foundation
  • Code Optimization
  • Assess and Debug Spark Applications
  • Spark UI Simulator 
  • Shuffles
  • Spill
  • Skew
  • Serialization
  • Fine-Tuning - Choosing the Right Cluster

Upcoming Public Classes

May 30
02 PM - 06 PM (Europe/London)
Jul 02
02 PM - 06 PM (Europe/London)
Jul 02
02 PM - 06 PM (America/New_York)
Jul 04
08 AM - 12 PM (Asia/Kolkata)
Jul 30
08 AM - 12 PM (Asia/Kolkata)
Jul 31
09 AM - 01 PM (America/Los_Angeles)

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

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Upcoming Public Classes

Data Engineer

Data Pipelines with Delta Live Tables

In this course, you'll use Delta Live Tables with your choice of Spark SQL or Python to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse. Learning objectives Describe how Delta Live Tables tracks data dependencies in data pipelines. Configure and run data pipelines using the Delta Live Tables UI. Use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables in the lakehouse using Auto Loader and Delta Live Tables. Use APPLY CHANGES INTO syntax to process Change Data Capture feeds. Review event logs and data artifacts created by pipelines and troubleshoot DLT syntaxPrerequisites Beginner familiarity with cloud computing concepts (virtual machines, object storage, etc.) Ability to perform basic code development tasks using the Databricks Data Engineering & Data Science workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc) Beginning programming experience with Delta Lake,Use Delta Lake DDL to create tables, compact files, restore previous table versions, and perform garbage collection of tables in the Lakehouse.Use CTAS to store data derived from a query in a Delta Lake table.Use SQL to perform complete and incremental updates to existing tables. Beginner programming experience with Python (syntax, conditions, loops, functions) Beginning programming experience with Spark SQL or PySpark. Extract data from a variety of file formats and data sources. Apply a number of common transformations to clean data. Reshape and manipulate complex data using advanced built-in functions. Production experience working with data warehouses and data lakes. Last course update April 2023
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If you have any questions, please refer to our Frequently Asked Questions page.