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)
1
Part 1:Unleashing Data Wrangling with AWS
3
Part 2:Data Wrangling with AWS Tools
7
Part 3:AWS Data Management and Analysis
12
Part 4:Advanced Data Manipulation and ML Data Optimization
15
Part 5:Ensuring Data Lake Security and Monitoring

Data Lake Security and Monitoring

In the previous chapters, we learned about the data wrangling process and how to utilize different services for data wrangling activities within the AWS ecosystem. By now, you should have enough knowledge on creating data pipelines using Glue DataBrew, SageMaker Data Wrangler, and AWS SDK for pandas. We also discussed the capabilities provided by AWS services such as AWS Glue, Athena, Amazon S3, and QuickSight, all of which support specific activities within your data wrangling pipeline more effectively. In this chapter, we will explore how to set up security and monitoring for the data wrangling pipeline for your workloads.

This chapter covers the following topics:

  • Data lake security
  • Monitoring and auditing