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

Data cleaning

Data cleaning is an important step in the process of data wrangling. A good amount of time is spent on identifying the right data source and cleaning the data. Pandas provides a lot of functionalities for cleaning your data.

The exact activities that are required during this phase are different for each type of dataset. Certain data sources will have data that requires only minimal cleaning and certain other data sources might require a lot of cleaning activities before the dataset can be used in your project. You could also use the output of data exploration activities to understand the level of cleaning activities to be performed on the data.

Data cleansing with Pandas

In order to demonstrate the data cleaning steps, we will use the seat_type table from our database. This table only has minimal data volume, so we will insert some data before we proceed with data cleansing.

The data in seat_type looks like the screenshot here. It has three columns for the...