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

Step 4 – adding transformations

As part of your data analysis, you might have noticed elements of your dataset that you want to change or transform. The goal of data transformation is to make data more suitable for modeling, to improve the performance of machine learning algorithms, or to handle missing or corrupted values. Data transformations for machine learning can include things such as normalization, standardization, data encoding, and binning. Not all datasets are alike, and not all transformations apply to all datasets. The goal of data analysis is to identify specific transformations for your dataset. While we typically apply data transformation as an early step in the machine learning pipeline, before data is used to train a model, in real-world machine learning, we continually monitor our model performance and apply transformations as necessary. After you have imported and inspected your dataset in Data Wrangler, you can start adding transformations to your data flow...