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

Ingesting and preparing data from a variety of sources

Amazon QuickSight can use other AWS services as a source, as well as on-premises databases, imported files, and even some Software as a Service (SaaS) applications.

For example, you can easily connect to Oracle, Microsoft SQL Server, Postgres, and MySQL databases, either running as part of the Amazon RDS managed database service or as instances running on Amazon EC2, or in your own data centers. You can also connect to data warehouse systems such as Amazon Redshift, Snowflake, and Teradata. Other AWS services are also supported as data sources, including Amazon S3, Amazon Athena, Amazon ElasticSearch Service, Amazon Aurora, and AWS IoT Analytics.

In addition to these traditional data sources, QuickSight can also connect to various SaaS offerings, including ServiceNow, Jira, Adobe Analytics, Salesforce, GitHub, and Twitter.

Data stored in files, such as a Microsoft Excel Spreadsheet (XLSX files), JSON documents, and...