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

Advanced data discovery and data structuring with Athena

In this section, we will explore how Amazon Athena helps in performing advanced data discovery and data structuring phases of the data wrangling pipeline.

SQL-based data discovery with Athena

Amazon Athena provides SQL-based data exploration and supports advanced data types as well. The advantage here is that even people with no coding expertise can explore data with familiar SQL syntax on multiple formats of data and different storage types.

Before we query data using Amazon Athena, we need to create metadata for the table in the Glue catalog. Amazon Athena helps you to query data from different data sources, as follows:

  • AWS Glue catalog—Access tables that are available in the AWS Glue catalog. We can create tables in the AWS Glue catalog in multiple ways, which we will explore later in this chapter.
  • Federated data sources—Supports querying data from Apache Hive, AWS, and third-party databases...