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Azure Data Engineer Associate Certification Guide

Azure Data Engineer Associate Certification Guide - Second Edition

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

Azure Data Engineer Associate Certification Guide

4.6 (16)
By: Giacinto Palmieri, Surendra Mettapalli, Newton 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

Partition Strategy for Efficiency and Performance

In the Benefits of Partitioning section, you learned how partitioning helps with performance, scale, security, availability, and so on.

The following are some strategies to be kept in mind while designing for efficiency and performance:

  • Partition datasets into smaller chunks that can be run with optimal parallelism for multiple queries.
  • Partition the data so that queries don’t end up requiring too much data from other partitions—that is, minimize cross-partition data transfers.
  • Design effective folder structures to improve the efficiency of data reads and writes. The following is the sample directory structure that you can create within ADLS Gen2 (Figure 2.4):
Figure 2.4 - An organizational diagram illustrates the hierarchical directory structure within Azure Data Lake Storage Gen2 for sales data. At the top level is “Sales data,” which branches into four subdirectories: “Raw data” with a nested “Source files” subdirectory, “Staging area,” “Processed data” and “Reports.” The structure is depicted with clear lines indicating the relationship and flow between each directory level.

Figure 2.4 – The directory structure within ADLS Gen2

  • Use descriptive names for files, reflecting their content, date, or purpose; for example, “sales_data_2024_q1.csv.” Also consider...
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Azure Data Engineer Associate Certification Guide
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