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 ingestion using AWS Glue ETL

In the previous section, we learned how to use various features of AWS Glue Crawler and AWS Glue Data Catalog to create a centralized data catalog for data discovery. In this section, we will explore the option of using AWS Glue ETL for data ingestion from various data sources, such as data lakes (Amazon S3), databases, streaming, and SaaS data stores. Additionally, we will learn about how to use job bookmarks to perform incremental data loads from Data Lake (S3) and JDBC.

Glue enables users to create ETL jobs using three different types of ETL frameworks – Spark ETL, Spark Streaming, and Python Shell. In the introduction section of Glue DataBrew, we learned how AWS Glue has evolved and that now, AWS Glue Studio is available to build ETL pipelines.

The AWS Glue user interface allows you to build your ETL pipeline with an interesting feature that converts your UI job into a script, which helps you scale when building similar pipelines or...