Overview of Course

The Apache Spark Application Performance Tuning course teaches you how to optimize Apache Spark applications for better performance, scalability, and efficiency. This course covers various aspects of tuning Spark applications, including memory management, resource allocation, and data serialization.

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Course Highlights

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Learn how to optimize Spark applications for better performance

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Understand the key components of Spark and how they affect performance

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Learn how to diagnose and troubleshoot performance issues in Spark applications




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Skills You’ll Learn

#1

Gain practical experience with various tools and techniques for tuning Spark applications

#2

Techniques for optimizing Apache Spark applications

#3

Best practices for memory management, resource allocation, and data serialization in Spark

#4

Diagnosing and troubleshooting performance issues in Spark applications

#5

Working with various tools and techniques for improving Spark application performance<br /><br />

Training Options

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1-on-1 Training

USD 2000 / INR 150000
  • Option Item Access to live online classes
  • Option Item Flexible schedule including weekends
  • Option Item Hands-on exercises with virtual labs
  • Option Item Session recordings and learning courseware included
  • Option Item 24X7 learner support and assistance
  • Option Item Book a free demo before you commit!
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On Request
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Course Reviews

Curriculum

  • RDDs
  • DataFrames and Datasets
  • Lazy Evaluation
  • Pipelining
     

  • Available Formats Overview
  • Impact on Performance
  • The Small Files Problem

  • The Cost of Inference
  • Mitigating Tactics
     

  • Recognizing Skew
  • Mitigating Tactics

  • Catalyst Overview
  • Tungsten Overview

  • Denormalization
  • Broadcast Joins
  • Map-Side Operations
  • Sort Merge Joins
     

  • Partitioned Tables
  • Bucketed Tables
  • Impact on Performance
     

  • Skewed Joins
  • Bucketed Joins
  • Incremental Joins

  • Pyspark Overhead
  • Scalar UDFs
  • Vector UDFs using Apache Arrow
  • Scala UDFs

  • Caching Options
  • Impact on Performance
  • Caching Pitfalls

  • WXM Overview
  • WXM for Spark Developers

  • Adaptive Number of Shuffle Partitions
  • Skew Joins
  • Convert Sort Merge Joins to Broadcast Joins
  • Dynamic Partition Pruning
  • Dynamic Coalesce Shuffle Partitions
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Description

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Target Audience:

  • Developers
  • Data Engineers
  • Data Scientists
  • Anyone who wants to learn how to optimize Apache Spark applications for better performance
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Prerequisite:

  • Knowledge of Apache Spark and Scala or Python programming languages
  • Familiarity with Linux command line
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Benefits of the course:

  • Gain a deeper understanding of Apache Spark and how it works
  • Learn how to optimize Spark applications for better performance
  • Gain practical experience with various tools and techniques for tuning Spark applications
  • Learn how to diagnose and troubleshoot performance issues in Spark applications
  • Boost your career prospects by adding Spark performance tuning skills to your resume
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Exam details to pass the course:

  • There is no exam for this course. You will receive a certificate of completion after finishing all the modules and quizzes.
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Certification path:

  • There are no specific certifications needed to learn this course. However, having a certification in Apache Spark can be beneficial.
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Career options after doing the course:

  • Big Data Engineer
  • Data Scientist
  • Data Analyst
  • Data Architect
  • Hadoop Developer

Why should you take this course from Skillzcafe:

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Why should you take this course from Skillzcafe:
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  • Bullet Icon Certificate of completion to showcase your skills

FAQs

No, this course is designed for intermediate and advanced level learners with prior knowledge of Apache Spark and Scala/Python programming.

You will need a computer with a minimum of 8GB RAM and Linux/MacOS operating system. You will also need to install Apache Spark and Scala/Python on

Spark application performance tuning can significantly improve the efficiency and scalability of Spark applications, enabling them to process larger datasets faster and with lower resource consumption. This can result in lower costs and faster time-to-insight for data-driven applications.

Participants should have prior experience with Apache Spark and basic knowledge of distributed systems, data processing, and programming in either Scala or Python.

This course covers a range of topics related to Spark application performance tuning, including memory management, data serialization, caching, shuffle optimization, parallelism, and troubleshooting performance issues.

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