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 discovery with AWS Glue

One of the unique features that sets AWS Glue apart from other ETL tools is its ability to create a centralized data catalog. This catalog is crucial for performing data discovery and relies on two important components of Glue:

  • Glue Data Catalog
  • Glue Data Crawler

AWS Glue Data Catalog

A data catalog is a centralized storage of metadata for data stored in different data stores, such as data lakes, data warehouses, relational databases, and non-relational databases. The metadata contains information about columns, data formats, locations, and serialization/deserialization mechanisms. Hive Metastore is one of the most popular metadata products used in the industry. However, it uses relational database management systems (RDBMSs) such as MySQL and PostgreSQL. The problem with using an RDBMS for Hive metadata is managing and maintaining it, especially for production workloads where high availability, scaling, and redundancy must be taken...