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

Product type Paperback

Published in Feb 2022

Publisher Packt

ISBN-13 9781801816069

Length 574 pages

Edition 1st Edition

Tools

Azure

Concepts

Big Data

Author (1):

Newton Alex

View More author details

Table of Contents (23) Chapters

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

Managing data pipelines in Data Factory/Synapse pipelines

ADF and Synapse pipelines provide two tabs called Manage and Monitor, which can help us manage and monitor the pipelines, respectively.

In the Manage tab, you can add, edit, and delete linked services, integration runtimes, triggers, configure Git, and more, as shown in the following screenshot:

Figure 11.9 – The Manage screen of ADF

We have already learned about linked services throughout this book. Now, let's explore the topic of integration runtimes in ADF and Synapse pipelines.

Integration runtimes

An integration runtime (IR) refers to the compute infrastructure that's used by ADF and Synapse Pipelines to run data pipelines and data flows. These are the actual machines or VMs that run the job behind the scenes.

The IR takes care of running data flows, copying data across public and private networks, dispatching activities to services such as Azure HDInsight and Azure...