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

Creating and sharing visuals with QuickSight analyses and dashboards

Once a dataset has been imported (and optionally transformed), you can create visualizations of this data using QuickSight analyses. This is the tool that is used by QuickSight authors to create new dashboards, with these dashboards containing one or more visualizations that can be shared with others in the business.

When you create a new analysis/dashboard, you choose one or more datasets to include in the analysis (up to a maximum of 50 datasets per dashboard). Each analysis consists of one or more sheets (or tabs, much like browser tabs) that display a group of visualizations. You can have up to 20 sheets (tabs) per dashboard, and each sheet can have up to 30 visualizations.

Once you have created an analysis (consisting of multiple visuals, optionally across multiple sheets), you can choose to publish the analysis as a dashboard. When you're publishing a dashboard, you can select various parameters related...