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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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Lock Free Chapter
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

Handling Failed Batch Loads

In data process management, two key strategies emerge: Reverting data to previous state and Configuring exception handling. While databases offer roll-back features for stability, ADF does not have built-in support for this. Instead, ADF focuses on consistency checks and fault tolerance settings to ensure data integrity.

For exception handling, connecting activities based on “success,” “failure,” “completion,” or “skip” outcomes provides control. Failed actions trigger fallback plans or alerts, ensuring pipeline continuity. Knowing about these mechanisms empowers smoother data workflows in both development and operational scenarios.

Note

This section primarily focuses on the Handle failed batch loads concept of the DP-203: Data Engineering on Microsoft Azure exam. The topic of what to do when batch data workloads fail has already been covered in Chapter 5, Developing a Batch Processing Solution...

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