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 transformation

Data processing for ML primarily includes data transformation. At its core, SageMaker Data Wrangler includes over 300 built-in transformations that are commonly used for cleaning, transforming, and featurizing your data specifically for data science and ML. Using these built-in transformations, you can transform columns within your dataset without having to write any code. In addition to these built-in transformations, you can add custom transformations using PySpark, Python, pandas, and PySpark SQL. Some of these transformations operate in place, while others create a new output column in your dataset. Whenever you incorporate a transform into your data flow, it introduces a new step in the process. Each added transform modifies your dataset and generates a fresh data frame as a result. Subsequently, any subsequent transforms you apply will be performed on this updated data frame. In the real world, datasets are often imbalanced. This imbalance can be in the form...