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Apache Spark Application Performance Tuning

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This hands-on training course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications. During the course, participants will learn how to identify common sources of poor performance in Spark applications, techniques for avoiding or solving them, and best practices for Spark application monitoring.

Apache Spark Application Performance Tuning presents the architecture and concepts behind Apache Spark and underlying data platform, then builds on this foundational understanding by teaching students how to tune Spark application code. The course format emphasizes instructor-led demonstrations illustrate both performance issues and the techniques that address them, followed by hands-on exercises that give students an opportunity to practice what they've learned through an interactive notebook environment.

The course applies to Spark 2.4, but also introduces the Spark 3.0 Adaptive Query Execution framework.

Course Contents

  • Spark Architecture
  • Data Sources and Formats
  • Inferring Schemas
  • Dealing With Skewed Data
  • Catalyst and Tungsten Overview
  • Mitigating Spark Shuffles
  • Partitioned and Bucketed Tables
  • Improving Join Performance
  • Pyspark Overhead and UDFs
  • Caching Data for Reuse
  • Workload XM (WXM) Introduction
  • What's New in Spark 3.0?

E-Book Symbol You will receive the original course documentation by Cloudera in English language as an E-Book (pdf).

Target Group

This course is designed for software developers, engineers, and data scientists who have experience developing Spark applications and want to learn how to improve the performance of their code. This is not an introduction to Spark.

Knowledge Prerequisites

Spark examples and hands-on exercises are presented in Python and the ability to program in this language is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

Course Objective

Students who successfully complete this course will be able to:

  • Understand Apache Spark's architecture, job execution, and how techniques such as lazy execution and pipelining can improve runtime performance
  • Evaluate the performance characteristics of core data structures such as RDD and DataFrames
  • Select the file formats that will provide the best performance for your application
  • Identify and resolve performance problems caused by data skew
  • Use partitioning, bucketing, and join optimizations to improve SparkSQL performance
  • Understand the performance overhead of Python-based RDDs, DataFrames, and user-defined functions
  • Take advantage of caching for better application performance
  • Understand how the Catalyst and Tungsten optimizers work
  • Understand how Workload XM can help troubleshoot and proactively monitor Spark applications performance
  • Learn about the new features in Spark 3.0 and specifically how the Adaptive Query Execution engine improves performance

Classroom training

Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!

Online training

You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.

Tailor-made courses

You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
Request for customized courses
PDF SymbolYou can find the complete description of this course with dates and prices ready for download at as PDF.

This hands-on training course delivers the key concepts and expertise developers need to improve the performance of their Apache Spark applications. During the course, participants will learn how to identify common sources of poor performance in Spark applications, techniques for avoiding or solving them, and best practices for Spark application monitoring.

Apache Spark Application Performance Tuning presents the architecture and concepts behind Apache Spark and underlying data platform, then builds on this foundational understanding by teaching students how to tune Spark application code. The course format emphasizes instructor-led demonstrations illustrate both performance issues and the techniques that address them, followed by hands-on exercises that give students an opportunity to practice what they've learned through an interactive notebook environment.

The course applies to Spark 2.4, but also introduces the Spark 3.0 Adaptive Query Execution framework.

Course Contents

  • Spark Architecture
  • Data Sources and Formats
  • Inferring Schemas
  • Dealing With Skewed Data
  • Catalyst and Tungsten Overview
  • Mitigating Spark Shuffles
  • Partitioned and Bucketed Tables
  • Improving Join Performance
  • Pyspark Overhead and UDFs
  • Caching Data for Reuse
  • Workload XM (WXM) Introduction
  • What's New in Spark 3.0?

E-Book Symbol You will receive the original course documentation by Cloudera in English language as an E-Book (pdf).

Target Group

This course is designed for software developers, engineers, and data scientists who have experience developing Spark applications and want to learn how to improve the performance of their code. This is not an introduction to Spark.

Knowledge Prerequisites

Spark examples and hands-on exercises are presented in Python and the ability to program in this language is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

Course Objective

Students who successfully complete this course will be able to:

  • Understand Apache Spark's architecture, job execution, and how techniques such as lazy execution and pipelining can improve runtime performance
  • Evaluate the performance characteristics of core data structures such as RDD and DataFrames
  • Select the file formats that will provide the best performance for your application
  • Identify and resolve performance problems caused by data skew
  • Use partitioning, bucketing, and join optimizations to improve SparkSQL performance
  • Understand the performance overhead of Python-based RDDs, DataFrames, and user-defined functions
  • Take advantage of caching for better application performance
  • Understand how the Catalyst and Tungsten optimizers work
  • Understand how Workload XM can help troubleshoot and proactively monitor Spark applications performance
  • Learn about the new features in Spark 3.0 and specifically how the Adaptive Query Execution engine improves performance

Classroom training

Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!

Online training

You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.

Tailor-made courses

You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
Request for customized courses

PDF SymbolYou can find the complete description of this course with dates and prices ready for download at as PDF.