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

Monitoring and auditing

In this section, we will look at different options for monitoring your data lake workloads. This will help you understand the performance and health of your data lake and other AWS services. We will also explore the auditing aspect of data lakes using Amazon CloudTrail.

Amazon CloudWatch

Amazon CloudWatch is a monitoring and observability service that provides users with visibility into the performance and health of their AWS resources and applications. It collects and stores metrics, logs, and events from a wide range of AWS resources, including EC2 instances, Lambda functions, RDS databases, and more. Users can then use this data to troubleshoot issues, optimize performance, and improve operational efficiency.

Overview of Amazon CloudWatch

  • Metrics: Numeric data points that represent the performance or utilization of a resource over time, such as CPU utilization, network traffic, or database latency
  • Logs: Text-based records of events and...