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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

Designing Partition Strategy for Analytical Workloads

The syllabus of the DP-203 exam is heavily skewed towards analytical workloads, as opposed to transactional workloads. While the latter is concerned with the creation, updating, or deletion of live production data, the former is concerned with trying to extract meaningful lessons from historical data (which mainly deals with data that is read only). The primary objective is to extract insights from historical data with data refresh and transformation activities for analytical purposes. In this context, the term “historical” could mean data from just last month or even last week.

Note

This section primarily focuses on the Implement a partition strategy for analytical workloads concept of the DP-203: Data Engineering on Microsoft Azure exam.

There are three main types of partition strategies for analytical workloads. These are as follows:

  • Horizontal partitioning (also known as sharding)
  • Vertical...
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