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

5 Vs of big data

The 5 Vs of big data are five key characteristics that define the concept of big data. These characteristics help to understand the nature of big data and how it can be effectively analyzed and used. Let’s look at these in more detail, as follows:

  • Volume: Big data refers to extremely large datasets that are too large to be processed using traditional methods. These datasets can range from a few terabytes to several petabytes in size.

For example, Twitter alone generates over 500 million tweets per day, which amounts to a large volume of data that must be stored, processed, and analyzed. Another example of big data would be data generated by large e-commerce companies such as Amazon. This data may include customer purchase history, website clickstream data, and customer service interactions. This data can be collected from various sources such as online transactions, mobile apps, social media, emails, and customer service interactions. All of this...