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 orchestration

Data orchestration can be defined as the process of combining data from various sources, including the steps to import, transform, and load it to the destination data source, with the fundamental principle being the ability to automate all the steps involved in the data preparation steps in a repeatable and reusable form, which can then be integrated with the overall ML pipelines. While data orchestration can be used in a wider context that can also include resource provisioning, scaling, and monitoring, the core of data orchestration is creating and automation data workflows, and this is where we will focus for the remainder of the book. The other heavy-lifting tasks of provisioning, scaling, and monitoring are taken care of by AWS. SageMaker Data Wrangler uses a data flow to connect the datasets and perform transformation and analysis steps. This data flow can be used to define your data pipeline and consists of all the steps that are involved in data preparation...