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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 Batch Size

Batch size refers to the number of data records processed together during a task, influencing the speed of task completion significantly. Using a larger batch size can enhance efficiency by minimizing data transfer and processing overhead. However, if the batch size is excessively large, it may strain system resources and impede performance. The optimal batch size is contingent upon the characteristics of the specific data being processed, including volume, complexity, and variability. For instance, a Copy activity is used to copy data between a Source and a sink, where the sink represents the destination.

Note

This section primarily focuses on the Configure the batch size concept of the DP-203: Data Engineering on Microsoft Azure exam.

When the sink is represented by a relational database, one option that can be specified is Write batch size, as shown in Figure 5.26, taken from an ADF pipeline:

Figure 5.26 – The image depicts the ADF data pipeline configuration interface, focusing on the “Copy data” activity. The central panel shows tabs for “General,” “Source,” “Sink,” “Mapping,” “Settings,” and “User properties,” with “Settings” active with “Write behavior” set to “insert” and “Write batch size” set to 100. The top of the interface has buttons for “Validate,” “Debug,” and “Add trigger” to run the pipeline.

Figure 5.26 – Specifying...

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