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

Configuration options for AWS SDK for pandas

There are options to configure global variables in awswrangler, which will help us to make the awswrangler library more customized for our specific use cases. We will look in depth at how to set up configurations to customize the working of awswrangler.

Setting up global variables

Global configuration variables can be set up through two different mechanisms in awswrangler:

  • Environment variables: Create environment variables in the operating system/kernel environment where the awswrangler package will be executed. For example, in a Jupyter notebook, use the %env command to set values for environment variables. This is loaded when the awswrangler object is instantiated.
  • Wrangler config variables: You can also set up values for environment variables using the wrangler object directly. Use the awswrangler.config.<<variable name>> syntax to assign a value for that configuration.

How does a config get applied...