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

Introduction to SageMaker Data Wrangler

Data processing is an integral part of machine learning (ML). In fact, it is not a stretch to say that ML models are only as good as the data that is used to train them. According to a Forbes survey from 2016, 80% of the time spent on an ML engineering project is data preparation. That is an astonishingly high percentage of time. Why is that the case? Due to the inherent characteristics of data in the real world, data preparation is both tedious and resource intensive. This real-world data is often referred to as dirty, unclean, noisy, or raw data in ML. In almost all cases, this is the type of data you begin your ML process with. Even in rare scenarios where you think you have good data, you still need to ensure that it is in the right format and scale it to be useful. Applying ML algorithms on this raw data would not give quality results as they would fail to identify patterns, detect anomalies correctly, or generalize well enough outside their...