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

Exploratory data analysis

Before we do any data transformation or manipulations, we need to get a good understanding of our data. Exploratory data analysis (EDA) is a crucial step in data science because it allows us to understand the structure and characteristics of the data we’re working with. EDA involves the use of various techniques and tools to summarize and visualize data in order to identify patterns, trends, and relationships. It is also important that we perform this step before we do any data transformations or modeling because EDA can help us understand which features are relevant and which are most important for the machine learning problem we are trying to solve. EDA can help you understand the distribution of data and identify any relationships that exist between the features in your dataset. When working with real-world data, you will inevitably encounter data quality issues such as missing data, imbalance in various classes, errors in data collection, and outliers...