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Azure Data Engineer Associate Certification Guide

You're reading from   Azure Data Engineer Associate Certification Guide Ace the DP-203 exam with advanced data engineering skills

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781805124689
Length 548 pages
Edition 2nd Edition
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Authors (3):
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Newton Alex Newton Alex
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Newton Alex
Giacinto Palmieri Giacinto Palmieri
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Giacinto Palmieri
Mr. Surendra Mettapalli Mr. Surendra Mettapalli
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Mr. Surendra Mettapalli
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Azure Basics FREE CHAPTER
2. Chapter 1: Introducing Azure Basics 3. Part 2: Data Storage
4. Chapter 2: Implementing a Partition Strategy 5. Chapter 3: Designing and Implementing the Data Exploration Layer 6. Part 3:Data Processing
7. Chapter 4: Ingesting and Transforming Data 8. Chapter 5: Developing a Batch Processing Solution 9. Chapter 6: Developing a Stream Processing Solution 10. Chapter 7: Managing Batches and Pipelines 11. Part 4:Secure, Monitor, and Optimize Data Storage and Processing
12. Chapter 8: Implementing Data Security 13. Chapter 9: Monitoring Data Storage and Data Processing 14. Chapter 10: Optimizing and Troubleshooting Data Storage and Data Processing 15. Chapter 11: Accessing the Online Practice Resources 16. Other Books You May Enjoy

Configuring Error Handling for the Transformation

In all the examples of ADF pipelines you used in this chapter, you have only seen success cases. However, ADF also provides a separate flow to handle errors and failures.

Note

This section primarily focuses on the Configure error handling for a transformation data concept of the DP-203: Data Engineering on Microsoft Azure exam.

In fact, ADF supports four different flows—Success, Failure, Completion, and Skipped—as shown in Figure 4.45:

Figure 4.45 – The image illustrates an Azure Data Factory (ADF) interface, illustrating a data flow diagram. It features a “Data flow” box labeled “ADFTransformations” connected to an “Add activity on” box. The latter includes options for “Success,” “Failure,” “Completion,” and “Skipped,” each with a corresponding-colored square. This setup suggests a workflow where subsequent activities are contingent on the outcomes of the data flow.

Figure 4.45 – ADF supporting four activity flows

If any errors are encountered at any step of the pipeline, you can build an error-handling branch that can be used to either fix the errors or store them for future actions. Figure 4.46 shows one such pipeline. You will have to connect the indicated line to the error-handling activity:

Figure 4.46 – The image illustrates an error-handling pipeline in a data processing environment. It shows a data flow with labeled boxes for “Data flow,” “Lookup,” and “Execute Pipeline,” connected by arrows to indicate the sequence of processes. The “Data flow” box is connected to the “Lookup” box, which in turn is connected to the “Execute Pipeline” box. A red arrow loops back from “Execute Pipeline” to “Data flow,” suggesting a retry mechanism in case of errors.

Figure 4.46 – Creating an error-handling pipeline

Select the Execute...

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