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Data Engineering with Scala and Spark

You're reading from   Data Engineering with Scala and Spark Build streaming and batch pipelines that process massive amounts of data using Scala

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781804612583
Length 300 pages
Edition 1st Edition
Languages
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Authors (3):
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Rupam Bhattacharjee Rupam Bhattacharjee
Author Profile Icon Rupam Bhattacharjee
Rupam Bhattacharjee
David Radford David Radford
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David Radford
Eric Tome Eric Tome
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Eric Tome
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
2. Chapter 1: Scala Essentials for Data Engineers FREE CHAPTER 3. Chapter 2: Environment Setup 4. Part 2 – Data Ingestion, Transformation, Cleansing, and Profiling Using Scala and Spark
5. Chapter 3: An Introduction to Apache Spark and Its APIs – DataFrame, Dataset, and Spark SQL 6. Chapter 4: Working with Databases 7. Chapter 5: Object Stores and Data Lakes 8. Chapter 6: Understanding Data Transformation 9. Chapter 7: Data Profiling and Data Quality 10. Part 3 – Software Engineering Best Practices for Data Engineering in Scala
11. Chapter 8: Test-Driven Development, Code Health, and Maintainability 12. Chapter 9: CI/CD with GitHub 13. Part 4 – Productionalizing Data Engineering Pipelines – Orchestration and Tuning
14. Chapter 10: Data Pipeline Orchestration 15. Chapter 11: Performance Tuning 16. Part 5 – End-to-End Data Pipelines
17. Chapter 12: Building Batch Pipelines Using Spark and Scala 18. Chapter 13: Building Streaming Pipelines Using Spark and Scala 19. Index 20. Other Books You May Enjoy

Performance Tuning

In the world of big data processing, Apache Spark has emerged as a powerful and versatile framework. Its ability to handle large-scale data processing tasks, coupled with its speed and ease of use, has made it a favorite among data engineers and analysts. However, as data volumes grow and processing demands become more complex, ensuring optimal performance becomes paramount. The Spark user interface (UI) plays a crucial role in this pursuit, offering insights and metrics that can guide performance tuning efforts by helping to identify undersized or oversized compute and issues with data.

In this chapter, we will delve into the intricacies of the Spark UI and explore how it can be leveraged to fine-tune the performance of your Spark applications.

We will cover the following topics:

  • Introducing the Spark UI
  • Leveraging the Spark UI for performance tuning
  • Right-sizing compute resources
  • Understanding data skewing, indexing, and partitioning
  • ...
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