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

Setting up a serverless data quality pipeline with Athena

Data quality validation is a very important step in data wrangling pipelines, ensuring the accuracy of data that will be used in analysis and visualization. We will explore in this section how to perform data quality validation through Amazon Athena.

Implementing data quality rules in Athena

Let us consider the rules that we want to validate in the NOAA weather dataset. What follows is only a high-level representation of some data quality rules and not a comprehensive ruleset for the weather dataset:

  1. The state column should have two character values when the country code is US.
  2. The date field shouldn’t have any future-dated values that would be incorrect measurements.
  3. Validate that the element column has only accepted the list of values as provided in the documentation.

We can have more rules that will ensure better data quality, but the preceding rules are sufficient for us to demonstrate...