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

Chapter 5: Architecting Data Engineering Pipelines

Having gained an understanding of data engineering principles, the core concepts, and the available AWS tools, we can now put these together in the form of a data pipeline. A data pipeline is the process that ingests data from multiple sources, optimizes and transforms the data, and makes it available to data consumers. An important function of the data engineering role is the ability to design, or architect, these pipelines.

In this chapter, we will cover the following topics:

  • Approaching the task of architecting a data pipeline
  • Identifying data consumers and understanding their requirements
  • Identifying data sources and ingesting data
  • Identifying data transformations and optimizations
  • Loading data into data marts
  • Wrapping up the whiteboarding session
  • Hands-on – architecting a sample pipeline