To get the most out of this book

Basic knowledge of computer systems and concepts, and how these are used within large organizations, is helpful prerequisite knowledge for this book. However, no data engineering-specific skills or knowledge are required. Also, a familiarity with cloud computing fundamentals and core AWS systems will make it easier to follow along, especially with the hands-on exercises, but detailed step-by-step instructions are included for each task.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Data-Engineering-with-AWS-2nd-edition. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781804614426.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Include a WHERE Year = 2020 clause.”

A block of code is set as follows:

datalake_bucket/year=2023/file1.parquet 
datalake_bucket/year=2022/file1.parquet 
datalake_bucket/year=2021/file1.parquet 
datalake_bucket/year=2020/file1.parquet

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

datalake_bucket/year=2023/file1.parquet
datalake_bucket/year=2022/file1.parquet
datalake_bucket/year=2021/file1.parquet
datalake_bucket/year=2020/file1.parquet

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “In addition, you can use Spark SQL to process data using standard SQL.”