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Data Wrangling on AWS

You're reading from   Data Wrangling on AWS Clean and organize complex data for analysis

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Product type Paperback
Published in Jul 2023
Publisher Packt
ISBN-13 9781801810906
Length 420 pages
Edition 1st Edition
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Authors (3):
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Sankar M Sankar M
Author Profile Icon Sankar M
Sankar M
Navnit Shukla Navnit Shukla
Author Profile Icon Navnit Shukla
Navnit Shukla
Sam Palani Sam Palani
Author Profile Icon Sam Palani
Sam Palani
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1:Unleashing Data Wrangling with AWS
2. Chapter 1: Getting Started with Data Wrangling FREE CHAPTER 3. Part 2:Data Wrangling with AWS Tools
4. Chapter 2: Introduction to AWS Glue DataBrew 5. Chapter 3: Introducing AWS SDK for pandas 6. Chapter 4: Introduction to SageMaker Data Wrangler 7. Part 3:AWS Data Management and Analysis
8. Chapter 5: Working with Amazon S3 9. Chapter 6: Working with AWS Glue 10. Chapter 7: Working with Athena 11. Chapter 8: Working with QuickSight 12. 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 14. Chapter 10: Data Processing for Machine Learning with SageMaker Data Wrangler 15. Part 5:Ensuring Data Lake Security and Monitoring
16. Chapter 11: Data Lake Security and Monitoring 17. Index 18. Other Books You May Enjoy

Data orchestration

Data orchestration can be defined as the process of combining data from various sources, including the steps to import, transform, and load it to the destination data source, with the fundamental principle being the ability to automate all the steps involved in the data preparation steps in a repeatable and reusable form, which can then be integrated with the overall ML pipelines. While data orchestration can be used in a wider context that can also include resource provisioning, scaling, and monitoring, the core of data orchestration is creating and automation data workflows, and this is where we will focus for the remainder of the book. The other heavy-lifting tasks of provisioning, scaling, and monitoring are taken care of by AWS. SageMaker Data Wrangler uses a data flow to connect the datasets and perform transformation and analysis steps. This data flow can be used to define your data pipeline and consists of all the steps that are involved in data preparation...

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