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Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide - Second Edition

By : Palmieri, Surendra Mettapalli, Alex
4.6 (16)
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Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide

4.6 (16)
By: Palmieri, Surendra Mettapalli, Alex

Overview of this book

One of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.
Table of Contents (17 chapters)
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1
Part 1: Azure Basics
3
Part 2: Data Storage
6
Part 3:Data Processing
11
Part 4:Secure, Monitor, and Optimize Data Storage and Processing

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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