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.
Understand data engineering concepts and emerging technologies
Ingest streaming data with Amazon Kinesis Data Firehose
Optimize, denormalize, and join datasets with AWS Glue Studio
Use Amazon S3 events to trigger a Lambda process to transform a file
Run complex SQL queries on data lake data using Amazon Athena
Load data into a Redshift data warehouse and run queries
Create a visualization of your data using Amazon QuickSight
Extract sentiment data from a dataset using Amazon Comprehend
A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.