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

Understanding Amazon Athena

Amazon Athena is an interactive, serverless analytical service that can help to explore data available in Amazon S3 without loading the data. Athena helps to analyze the data through SQL syntax or interactive Spark applications. It can help customers to analyze datasets stored in multiple data formats on Amazon S3 through familiar SQL. Since Athena is a serverless offering, users are charged based on the data scanned by the queries and there is no need to maintain separate servers to enable user queries. This has enabled business and analytics teams to quickly analyze, transform, and visualize data in the data lake for specific use cases without relying on the data engineering team to come up with complex ETL pipelines.

Amazon Athena was launched on re:Invent 2016 and has gone through significant improvements over the course of time. It was launched as a serverless mechanism to query Amazon S3 data using managed Presto servers from the Athena catalog...