Cloudera/Apache Spark Application Performance Tuning

Apache Spark Application Performance Tuning Certification Training Course Overview

This three-day 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.

Target Audience

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.

Learning Objective

  • 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

Apache Spark Application Performance Tuning (24 Hours) Download Course Contents

Live Virtual Classroom Fee On Request
Group Training
18 - 20 Oct 09:00 AM - 05:00 PM CST
(8 Hours/Day)

01 - 03 Nov 09:00 AM - 05:00 PM CST
(8 Hours/Day)

06 - 08 Dec 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR)
4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

GTR=Guaranteed to Run
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request
Special Solutions for Corporate Clients! Click here
Hire Our Trainers! Click here

Course Modules

Module 1: Spark Architecture
  • RDDs
  • DataFrames and Datasets
  • Lazy Evaluation
  • Pipelining
Module 2: Data Sources and Formats
  • Available Formats Overview
  • Impact on Performance
  • The Small Files Problem
Module 3: Inferring Schemas
  • The Cost of Inference
  • Mitigating Tactics
Module 4: Dealing With Skewed Data
  • Recognizing Skew
  • Mitigating Tactics
Module 5: Catalyst and Tungsten Overview
  • Catalyst Overview
  • Tungsten Overview
Module 6: Mitigating Spark Shuffles
  • Denormalization
  • Broadcast Joins
  • Map-Side Operations
  • Sort Merge Joins
Module 7: Partitioned and Bucketed Tables
  • Partitioned Tables
  • Bucketed Tables
  • Impact on Performance
Module 8: Improving Join Performance
  • Skewed Joins
  • Bucketed Joins
  • Incremental Joins
Module 9: Pyspark Overhead and UDFs
  • Pyspark Overhead
  • Scalar UDFs
  • Vector UDFs using Apache Arrow
  • Scala UDFs
Module 10: Caching Data for Reuse
  • Caching Options
  • Impact on Performance
  • Caching Pitfalls
Module 11: Workload XM (WXM) Introduction
  • WXM Overview
  • WXM for Spark Developers
Module 12: What's New in Spark 3.0?
  • Adaptive Number of Shuffle Partitions
  • Skew Joins
  • Convert Sort Merge Joins to Broadcast Joins
  • Dynamic Partition Pruning
  • Dynamic Coalesce Shuffle Partitions
Download Course Contents

Request More Information

Course 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.


Yes, fee excludes local taxes.