Book Image

Data Wrangling on AWS

By : Navnit Shukla, Sankar M, Sampat Palani
5 (1)
Book Image

Data Wrangling on AWS

5 (1)
By: Navnit Shukla, Sankar M, Sampat Palani

Overview of this book

Data wrangling is the process of cleaning, transforming, and organizing raw, messy, or unstructured data into a structured format. It involves processes such as data cleaning, data integration, data transformation, and data enrichment to ensure that the data is accurate, consistent, and suitable for analysis. Data Wrangling on AWS equips you with the knowledge to reap the full potential of AWS data wrangling tools. First, you’ll be introduced to data wrangling on AWS and will be familiarized with data wrangling services available in AWS. You’ll understand how to work with AWS Glue DataBrew, AWS data wrangler, and AWS Sagemaker. Next, you’ll discover other AWS services like Amazon S3, Redshift, Athena, and Quicksight. Additionally, you’ll explore advanced topics such as performing Pandas data operation with AWS data wrangler, optimizing ML data with AWS SageMaker, building the data warehouse with Glue DataBrew, along with security and monitoring aspects. By the end of this book, you’ll be well-equipped to perform data wrangling using AWS services.
Table of Contents (19 chapters)
Part 1:Unleashing Data Wrangling with AWS
Part 2:Data Wrangling with AWS Tools
Part 3:AWS Data Management and Analysis
Part 4:Advanced Data Manipulation and ML Data Optimization
Part 5:Ensuring Data Lake Security and Monitoring

Data discovery with QuickSight

Amazon QuickSight supports loading data from various data sources, and we can then create visuals in the Analyses tab to understand the data. Data discovery can also be done using Jupyter notebooks with custom visualization libraries, but that might require programming expertise and complex setup before performing data discovery activities. In contrast, business users can perform data discovery in QuickSight with visuals in the Analyses tab.

QuickSight-supported data sources and setup

QuickSight supports a wide variety of data sources. The complete list can be found at

The sources could be classified into the following broad categories:

  • Relational data sources: Covering cloud and on-premises data sources, including popular data engines such as MySQL, Postgres, SQL Server, Oracle, Snowflake, and Redshift. When connecting to on-premises data sources, you need...