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 visualization with QuickSight

In this section, we will explore different visuals and options that are available within QuickSight. We will explore high-level concepts and different visuals and features within QuickSight in this section.

Visualization and charts with QuickSight

The following steps are performed when publishing a dashboard in QuickSight.

Figure 8.24: QuickSight – high-level dashboard publishing workflow

Figure 8.24: QuickSight – high-level dashboard publishing workflow

  1. Create data source: This is the step where the connection to the data source is established. This can be a one-time activity and can be reused multiple times when new datasets are created from the data source.
  2. Create dataset: A dataset can be created from a new data source or existing datasets for different analysis requirements. The datasets can be modified, parsed, or enriched based on specific analysis requirements.
  3. Create analysis (create visuals): Create visuals from a specific dataset. Here, the dashboard...