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


In this chapter, we discussed the options for securing, monitoring, and auditing your data pipeline within AWS. Earlier in this book, we explored various options for performing data wrangling activities within AWS. Let’s summarize the data we discussed earlier in this book:

  • First, we explored AWS Glue DataBrew, which helps you create a data wrangling pipeline through a GUI-based approach for every type of user. This is useful for teams who want to quickly set up a data wrangling pipeline without worrying about the coding and management aspects of the pipeline.
  • We also covered SageMaker Data Wrangler, which helps users create a GUI-based data wrangling pipeline. However, it’s more closely aligned toward machine learning workloads with tighter integration with SageMaker services. This is useful for teams who are planning to manage data wrangling for model training and inference in SageMaker.
  • We also explored AWS SDK for pandas, also known as awswrangler...