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 export

So far, we’ve looked at Data Wrangler capabilities that enable you to import data into Data Wrangler and perform analysis and transformations. SageMaker Data Wrangler enables you to export all or part of these transformations as a data flow. In most cases, data processing consists of a series of transformations. Each of these transformations can be referred to as a step in Data Wrangler. A Data Wrangler flow is made up of a series of nodes that represent the import of your data and the transformations that you’ve performed. As we covered earlier, one of the first steps in Data Wrangler is to import data from a supported data source. As such, the data source is the first node in your data flow. Following the previous step, the next node in the data flow is the Data Types node. This node signifies that Data Wrangler has executed a transformation to convert the dataset into a format that is suitable for further analysis and processing. Each transformation that...