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 orchestrating big data pipelines

As discussed in Chapter 2, Data Management Architectures for Analytics, a data pipeline can be built to bring in data from source systems, and then transform that data, often moving the data through multiple stages, further transforming or enriching the data as it moves through each stage.

An organization will often have tens or hundreds of pipelines that work independently or in conjunction with each other on different datasets and perform different types of transformations. Each pipeline may use multiple services to achieve the goals of the pipeline and orchestrating all the varying services and pipelines can be complex. In this section, we look at a number of AWS services that help with this orchestration task.

Overview of AWS Glue workflows for orchestrating Glue components

In the AWS services for transforming data section, we covered AWS Glue, a service that includes a number of components. As a reminder, they are as follows...