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 looked at a critical part of a data engineers' job: designing and orchestrating data pipelines. First, we examined some of the core concepts around data pipelines, such as scheduled and event-based pipelines, and how to handle failures and retries.

We then looked at four different AWS services that can be used for creating and orchestrating data pipelines. This included Amazon Data Pipeline, AWS Glue Workflows, Amazon Managed Workflows for Apache Airflow (MWAA), and AWS Step Function. We discussed some of the use cases for each of these services, as well as the pros and cons of them.

Then, in the hands-on section of this chapter, we built an event-driven pipeline. We used two AWS Lambda functions for processing and an Amazon SNS topic for sending out notifications about failure. Then, we put these pieces of our data pipeline together into a state machine orchestrated by AWS Step Function. We also looked at how to handle errors.

So far, we have...