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

Data discovery is an important phase in the wrangling pipeline, as it helps users to understand the data and guides how the next steps should be done. For example, if the user looks at the data and determines certain columns have missing values, data cleansing should fix those values and any missing columns can be added by joining the data with other data sources or deriving them from raw data. Essentially, this step will give an idea of the completeness, usefulness, and relevance of the dataset to users.

There are multiple ways to perform data discovery including downloading small files on a local machine and using Excel files to explore the data. We will look at ways in which we can explore the raw data stored in a data lake. Some of the common steps that are performed during a data discovery phase are as follows:

  • Identifying the source data structure/format and its associated properties
  • Visualizing the data distribution on the dataset
  • Validating...