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

Summary

In this chapter, we reviewed an approach to developing data engineering pipelines by identifying a limited-scope project, and then whiteboarding a high-level architecture diagram. We looked at how we could have a workshop, in conjunction with relevant stakeholders in the organization, to discuss requirements and plan the initial architecture.

We approached this task by working backward. We started by identifying who the data consumers of the project would be and learning about their requirements. Then, we looked at which data sources could be used to provide the required data and how those data sources could be ingested. We then reviewed, at a high level, some of the data transformations that would be required for the project to optimize the data for analytics.

In the next chapter, we will take a deeper dive into the AWS services for ingesting batch and streaming data as part of our data engineering pipeline.