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

Why AWS Glue DataBrew?

Having the right tools is a crucial factor in enabling organizations to become data-driven, and AWS Glue DataBrew is one such tool. It is part of the AWS Glue family, which was introduced at re:Invent 2020.

Initially, when AWS Glue was launched in August 2017, it was targeted at developers and data engineers who were writing Apache Spark code. The goal was to provide them with a platform that offered both compute and storage resources to run their Spark code. This allowed them to take advantage of the speed and ease of use of Apache Spark, which is 100 times faster than Hadoop for large-scale data processing, while also leveraging the benefits of the cloud, such as elasticity, performance, and cost-effectiveness.

As the adoption of the public cloud increased and became more mainstream over time, AWS Glue evolved to meet the changing needs of enterprises. Initially, it was primarily used as an ETL tool, but it has since expanded to become a more comprehensive...