Databricks Performance Optimization

In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.

Languages Available: English | 日本語 | Português BR | 한국어

Skill Level

Professional

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

• Ability to perform basic code development tasks using Databricks (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.)

• Intermediate programming experience with PySpark, such as extract data from a variety of file format data sources, apply a number of common transformations to clean data, and reshape and manipulate complex data using advanced built-in functions

• Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions, etc.)

Outline

Spark Architecture

⇾ Spark UI Introduction

Designing the Foundation

⇾ File Explosion

⇾ Data Skipping and Liquid Clustering

Code Optimization

⇾ Skew

⇾ Shuffle

⇾ Spill

⇾ Exploding Join

⇾ Serialization

⇾ User-Defined Functions

Fine-Tuning - Choosing the Right Cluster

⇾ Fine-Tuning: Choosing the Right Cluster

⇾ Pick the Best Instance Types

Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 12	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jun 16	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jul 17	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 17	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

Date	Time	Your Local Time	Language	Price
Date	Time	Your Local Time	Language	Price	Jun 12	01 PM - 05 PM (Europe/London)	-	English	$750.00
Jun 16	08 AM - 12 PM (Asia/Kolkata)	-	English	$750.00
Jul 17	01 PM - 05 PM (Australia/Sydney)	-	English	$750.00
Jul 17	09 AM - 01 PM (America/New_York)	-	English	$750.00

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Upcoming Public Classes

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You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with Spark Declarative Pipeline and Jobs to ensure reliability.

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By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.