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Data Engineering with Scala and Spark

You're reading from  Data Engineering with Scala and Spark

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781804612583
Pages 300 pages
Edition 1st Edition
Languages
Authors (3):
Eric Tome Eric Tome
Profile icon Eric Tome
Rupam Bhattacharjee Rupam Bhattacharjee
Profile icon Rupam Bhattacharjee
David Radford David Radford
Profile icon David Radford
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1 – Introduction to Data Engineering, Scala, and an Environment Setup
2. Chapter 1: Scala Essentials for Data Engineers 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

Understanding data skewing, indexing, and partitioning

Like with any data processing system, all of the greatest hardware will only produce mediocre results. There is no magic bullet that will solve poor data layouts. The fastest disk, processing chips, and network will not negate the need to plan for well-thought-out indexing and partitioning strategies. Data skew can sneak into processing pipelines or queries and bring them to a crawl. These three critical aspects need to be planned for and monitored to prevent degradation to data processing and querying. We’ll learn more about them in the following sections.

Data skew

Data skew is a common problem when utilizing distributed data systems such as Apache Spark. It will show up when some processing partitions are significantly larger than others, resulting in some tasks finishing quickly while waiting for others to complete. This can result in under-utilized compute, long processing times, and out-of-memory errors. Joins...

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