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

AWS services for transforming data

Once your data is ingested into an appropriate AWS service, such as Amazon S3, the next stage of the pipeline needs to transform the data to optimize it for analytics and to make it available to your data consumers.

Some of the tools we discussed in the previous section for ingesting data into AWS can perform light transformations as part of the ingestion process. For example, Amazon DMS can write out data in Parquet format (a format optimized for analytics), as can Kinesis Firehose. However, heavier transformations are often required to fully optimize your data for a differing set of analytic tasks and diverse data consumers, and in this section, we will examine some of the core AWS services that can be used for this.

Overview of AWS Lambda for light transformations

AWS Lambda provides a serverless environment for executing code and is one of AWS's most popular services. You can trigger your Lambda function to execute your code in multiple...