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Data Engineering with AWS

Data Engineering with AWS - Second Edition

By : Gareth Eagar
4.8 (31)
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Data Engineering with AWS

Data Engineering with AWS

4.8 (31)
By: Gareth Eagar

Overview of this book

This book, authored by a Senior Data Architect with 25 years of experience, helps you gain expertise in the AWS ecosystem for data engineering. This revised edition updates every chapter to cover the latest AWS services and features, provides a refreshed view on data governance, and introduces a new section on building modern data platforms. You will learn how to implement a data mesh, work with open-table formats such as Apache Iceberg, and apply DataOps practices for automation and observability. You will begin by exploring core concepts and essential AWS tools used by data engineers, along with modern data management approaches. You will then design and build data pipelines, review raw data sources, transform data, and understand how it is consumed by various stakeholders. The book also covers data governance, populating data marts and warehouses, and how a data lakehouse fits into the architecture. You will explore AWS tools for analysis, SQL queries, visualizations, and learn how AI and machine learning generate insights from data. Later chapters cover transactional data lakes, data meshes, and building a complete AWS data platform. By the end, you will be able to confidently implement data engineering pipelines on AWS. *Email sign-up and proof of purchase required
Table of Contents (24 chapters)
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1
Section 1: AWS Data Engineering Concepts and Trends
6
Section 2: Architecting and Implementing Data Engineering Pipelines and Transformations
13
Section 3: The Bigger Picture: Data Analytics, Data Visualization, and Machine Learning
17
Section 4: Modern Strategies: Open Table Formats, Data Mesh, DataOps, and Preparing for the Real World
22
Other Books You May Enjoy
23
Index

Ingesting Batch and Streaming Data

Having developed a high-level architecture for our data pipeline, we can now dive deep into the varied components of the architecture. We will start with data ingestion so that in the hands-on section of this chapter, we can ingest data and use that data for the hands-on activities in future chapters.

Data engineers are often faced with the challenge of the five Vs of data. These are the variety of data (the diverse types and formats of data); the volume of data (the size of the dataset); the velocity of the data (how quickly the data is generated and needs to be ingested); the veracity or validity of the data (the quality, completeness, and credibility of the data); and finally, the value of data (the value that the data can provide the business with).

In this chapter, we will look at several different types of data sources and examine the various tools available within AWS for ingesting data from these sources. We will also look at how...

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Data Engineering with AWS
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