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Data Engineering with AWS

Data Engineering with AWS

By : Gareth Eagar
4.7 (24)
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Data Engineering with AWS

Data Engineering with AWS

4.7 (24)
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)
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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

Summary

In this chapter, we had a deeper look at the Amazon Athena service, which is an AWS-managed version of Apache Presto. We looked at how to optimize our data and queries to increase query performance and reduce costs.

Then, we explored how Athena can be used as a SQL query engine – not only for data in an Amazon S3 data lake but also for external data sources such as other database systems, data warehouses, and even CloudWatch logs using Athena Query Federation.

Finally, we explored Athena Workgroups, which let us manage governance and costs. Workgroups can be used to enforce specific settings for different teams or projects, and can also be used to limit the amount of data that's scanned by queries.

In the next chapter, we will take a deeper dive into another Amazon tool for data consumers as we look at how we can create rich visualizations and dashboards using Amazon QuickSight.

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Data Engineering with AWS
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