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

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781804612583

Length 300 pages

Edition 1st Edition

Languages

Scala

Concepts

Data Engineering

Authors (3):

Rupam Bhattacharjee

David Radford

Eric Tome

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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 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

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Data Profiling and Data Quality

As we work with multiple sources of data, it is quite easy for some bad data to pass through if there are no checks in place. This can lead to serious issues in downstream systems that rely on the accuracy of upstream data to build models, run business-critical applications, and so on. To make our data pipelines resilient, it is imperative that we have data quality checks in place to ensure the data being processed meets the requirements imposed by both business as well as downstream applications.

Six primary data quality dimensions can be measured individually and used to improve the data quality:

Completeness: Does your customer dataset that you plan to use for an upcoming marketing campaign have all of the required attributes filled in?
Accuracy: Are the email addresses and phone numbers accurate for your customer records?
Consistency: Is customer data consistent across systems?
Validity: Do your customer records have valid...