You're reading from Data Engineering with AWS - Second Edition

Product type Book

Published in Oct 2023

Publisher Packt

ISBN-13 9781804614426

Pages 636 pages

Edition 2nd Edition

Languages

Concepts

Data Engineering

Author (1):

Gareth Eagar

Table of Contents (24) Chapters

Preface

1. Section 1: AWS Data Engineering Concepts and Trends

2. An Introduction to Data Engineering

3. Data Management Architectures for Analytics

4. The AWS Data Engineer’s Toolkit

5. Data Governance, Security, and Cataloging

6. Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations

7. Architecting Data Engineering Pipelines

8. Ingesting Batch and Streaming Data

9. Transforming Data to Optimize for Analytics

10. Identifying and Enabling Data Consumers

11. A Deeper Dive into Data Marts and Amazon Redshift

12. Orchestrating the Data Pipeline

13. Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

14. Ad Hoc Queries with Amazon Athena

15. Visualizing Data with Amazon QuickSight

16. Enabling Artificial Intelligence and Machine Learning

17. Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World

18. Building Transactional Data Lakes

19. Implementing a Data Mesh Strategy

20. Building a Modern Data Platform on AWS

21. Wrapping Up the First Part of Your Learning Journey

22. Other Books You May Enjoy

23. Index

Identifying data transformations and optimizations

In a typical data analytics project, we ingest data from multiple data sources and then perform transforms on those datasets to optimize them for the required analytics.

In Chapter 7, Transforming Data to Optimize for Analytics, we will do a deeper dive into typical transformations and optimizations, but we will provide a high-level overview of the most common transformations here.

File format optimizations

CSV, XML, JSON, and other types of plaintext files are commonly used to store structured and semi-structured data. These file formats are useful when manually exploring data, but there are much better, binary-based file formats to use for computer-based analytics. A common binary format that is optimized for read-heavy analytics (such as by compressing data and adding in useful metadata to optimize data reads) is the Apache Parquet format. A common transformation is to convert plaintext files into an optimized format...