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Azure Data Engineer Associate Certification Guide

You're reading from   Azure Data Engineer Associate Certification Guide A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam

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Product type Paperback
Published in Feb 2022
Publisher Packt
ISBN-13 9781801816069
Length 574 pages
Edition 1st Edition
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Author (1):
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Newton Alex Newton Alex
Author Profile Icon Newton Alex
Newton Alex
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1: Azure Basics
2. Chapter 1: Introducing Azure Basics FREE CHAPTER 3. Part 2: Data Storage
4. Chapter 2: Designing a Data Storage Structure 5. Chapter 3: Designing a Partition Strategy 6. Chapter 4: Designing the Serving Layer 7. Chapter 5: Implementing Physical Data Storage Structures 8. Chapter 6: Implementing Logical Data Structures 9. Chapter 7: Implementing the Serving Layer 10. Part 3: Design and Develop Data Processing (25-30%)
11. Chapter 8: Ingesting and Transforming Data 12. Chapter 9: Designing and Developing a Batch Processing Solution 13. Chapter 10: Designing and Developing a Stream Processing Solution 14. Chapter 11: Managing Batches and Pipelines 15. Part 4: Design and Implement Data Security (10-15%)
16. Chapter 12: Designing Security for Data Policies and Standards 17. Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
18. Chapter 13: Monitoring Data Storage and Data Processing 19. Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing 20. Part 6: Practice Exercises
21. Chapter 15: Sample Questions with Solutions 22. Other Books You May Enjoy

Chapter 11: Managing Batches and Pipelines

Welcome to Chapter 11! This is one of the smaller and easier chapters and will be a breeze to read through. In this chapter, we will be focusing on four broad categories: triggering Batch jobs, handling failures in Batch jobs, managing pipelines, and configuring version control for our pipelines. Once you have completed this chapter, you should be able to comfortably set up and manage Batch pipelines using Azure Batch, Azure Data Factory (ADF), or Synapse pipelines.

In this chapter, we will cover the following topics:

  • Triggering Batches
  • Handling failed Batch loads
  • Validating Batch loads
  • Managing data pipelines in Data Factory/Synapse pipelines
  • Scheduling data pipelines in Data Factory/Synapse pipelines
  • Managing Spark jobs in a pipeline
  • Implementing version control for pipeline artifacts
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