Book Image

Data Engineering with AWS

By : Gareth Eagar
Book Image

Data Engineering with AWS

By: Gareth Eagar

Overview of this book

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.
Table of Contents (19 chapters)
1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Lakes and Data Lake Houses
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning

Types of data transformation tools

As we covered in Chapter 3, The AWS Data Engineer's Toolkit, there are a number of AWS services that can be used for data transformation. We reviewed a number of these services in Chapter 3, The AWS Data Engineer's Toolkit, so make sure to review that chapter, but in this section, we will look more broadly at the different types of data transformation engines.

Apache Spark

Apache Spark is an in-memory engine for working with large datasets, providing a mechanism to split a dataset among multiple nodes in a cluster for efficient processing. Spark is an extremely popular engine to use for processing and transforming big datasets, and there are multiple ways to run Spark jobs within AWS.

With Apache Spark, you can either process data in batches (such as on a daily basis or every few hours) or process near real-time streaming data using Spark Streaming. In addition, you can use Spark SQL to process data using standard SQL and Spark...