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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

Summary

In this chapter, you explored several new concepts, some of which may require considerable time and effort to fully grasp. Tasks such as handling skew in data, data spill, tuning queries, and troubleshooting failed pipelines and jobs are complex enough to warrant their own books. An overview of these topics was provided, along with additional resources for further exploration.

You learned about essential concepts for efficient Big Data Analytics, by addressing the issue of small files, as well as data compaction techniques to improve storage efficiency and query performance. After that, you explored strategies for handling data skew and spills, which is crucial for optimizing SQL and Spark environments, and then examined shuffle partitions in Spark, where techniques such as indexing and caching for performance enhancement were discussed. Additionally, you saw general tips for resource management and guidelines for debugging Spark jobs.

By now, you should have a solid...

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