Start your AWS data engineering journey with this easy-to-follow, hands-on guide and get to grips with foundational concepts through to building data engineering pipelines using AWS
- Learn about common data architectures and modern approaches to generating value from big data
- Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines
- Learn how to architect and implement data lakes and data lakehouses for big data analytics
Knowing how to architect and implement complex data pipelines is a highly sought-after skill. Data engineers are responsible for building these pipelines that ingest, transform, and join raw datasets – creating new value from the data in the process.
Amazon Web Services (AWS) offers a range of tools to simplify a data engineer’s job, making it the preferred platform for performing data engineering tasks.
This book will take you 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. The book also teaches you 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.
What you will learn
- 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