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)
1
Part 1:Unleashing Data Wrangling with AWS
3
Part 2:Data Wrangling with AWS Tools
7
Part 3:AWS Data Management and Analysis
12
Part 4:Advanced Data Manipulation and ML Data Optimization
15
Part 5:Ensuring Data Lake Security and Monitoring

Data import

Before you start to process your data using SageMaker Data Wrangler, you first need to import data into Data Wrangler. Using Data Wrangler, you can connect and import data from a variety of data stores. When you start Data Wrangler for the first time, the first screen you get asks whether you want to import data or use a sample dataset:

Figure 4.1 – Data Wrangler import

Figure 4.1 – Data Wrangler import

Amazon S3 is an object-based data store that has quickly become the de facto storage of the internet. Due to its low cost per GB and high levels of reliability, you can store and retrieve any amount of data, at any time, from anywhere on the web using Amazon S3. You can upload and access data both using the console or programmatically using APIs, which is also the most common way to work with data in Amazon S3. Amazon S3 implements bucket and object architecture. You can think of a bucket as a folder and objects as files that are logically stored inside the bucket. SageMaker...