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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1:Unleashing Data Wrangling with AWS
Part 1:Unleashing Data Wrangling with AWS
Free Chapter
2
Chapter 1: Getting Started with Data Wrangling
Chapter 1: Getting Started with Data Wrangling
Introducing data wrangling
The steps involved in data wrangling
Best practices for data wrangling
Options available for data wrangling on AWS
Summary
3
Part 2:Data Wrangling with AWS Tools
Part 2:Data Wrangling with AWS Tools
4
Chapter 2: Introduction to AWS Glue DataBrew
Chapter 2: Introduction to AWS Glue DataBrew
Why AWS Glue DataBrew?
Getting started with AWS Glue DataBrew
Using AWS Glue DataBrew for data wrangling
Data protection with AWS Glue DataBrew
Data lineage and data publication
Summary
5
Chapter 3: Introducing AWS SDK for pandas
Chapter 3: Introducing AWS SDK for pandas
AWS SDK for pandas
Building blocks of AWS SDK for pandas
Customizing, building, and installing AWS SDK for pandas for different use cases
Configuration options for AWS SDK for pandas
The features of AWS SDK for pandas with different AWS services
Summary
6
Chapter 4: Introduction to SageMaker Data Wrangler
Chapter 4: Introduction to SageMaker Data Wrangler
Data import
Data orchestration
Data transformation
Insights and data quality
Data analysis
Data export
SageMaker Studio setup prerequisites
Summary
7
Part 3:AWS Data Management and Analysis
Part 3:AWS Data Management and Analysis
8
Chapter 5: Working with Amazon S3
Chapter 5: Working with Amazon S3
What is big data?
5 Vs of big data
What is a data lake?
Data lake layouts
Challenges and considerations when building a data lake on Amazon S3
Summary
9
Chapter 6: Working with AWS Glue
Chapter 6: Working with AWS Glue
What is Apache Spark?
Data discovery with AWS Glue
Data ingestion using AWS Glue ETL
Summary
10
Chapter 7: Working with Athena
Chapter 7: Working with Athena
Understanding Amazon Athena
Advanced data discovery and data structuring with Athena
Enriching data from multiple sources using Athena
Setting up a serverless data quality pipeline with Athena
Summary
11
Chapter 8: Working with QuickSight
Chapter 8: Working with QuickSight
Introducing Amazon QuickSight and its concepts
Data discovery with QuickSight
Data visualization with QuickSight
Summary
12
Part 4:Advanced Data Manipulation and ML Data Optimization
Part 4:Advanced Data Manipulation and ML Data Optimization
13
Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas
Chapter 9: Building an End-to-End Data-Wrangling Pipeline with AWS SDK for Pandas
A solution walkthrough for sportstickets.com
Data discovery
Data structuring
Data cleaning
Data enrichment
Data quality validation
Data visualization
Summary
14
Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler
Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler
Technical requirements
Step 1 – logging in to SageMaker Studio
Step 2 – importing data
Exploratory data analysis
Step 4 – adding transformations
Step 5 – exporting data
Training a machine learning model
Summary
15
Part 5:Ensuring Data Lake Security and Monitoring
Part 5:Ensuring Data Lake Security and Monitoring
16
Chapter 11: Data Lake Security and Monitoring
Chapter 11: Data Lake Security and Monitoring
Data lake security
Monitoring and auditing
Summary
17
Index
Index
Why subscribe?
18
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To get the most out of this book

To get the most out of this book, it is helpful to have a basic understanding of data concepts and familiarity with data manipulation techniques. Some prior exposure to programming languages, such as Python or SQL, will also be beneficial, as we will be utilizing these languages for data-wrangling tasks. Additionally, a foundational understanding of cloud computing and AWS will aid in grasping the concepts and tools discussed throughout the book. While not essential, having hands-on experience with AWS services such as Amazon S3 and AWS Glue will further enhance your learning experience. By having these prerequisites in place, you will be able to fully engage with the content and successfully apply the techniques and practices covered in Data Wrangling on AWS.

Software/hardware covered in the book

Operating system requirements

AWS

Windows, macOS, or Linux

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

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