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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

Batch-Processing Technologies

Consider, for instance, the case of credit card processing. First and most obviously, it will involve the processing of the credit card transaction in real time, as the buyer and the seller will need to know as quickly as possible whether the transaction was approved. This is an example of stream processing. On the other hand, if the credit card company sends a statement at the end of the month with all the transactions made by the cardholder, that is an example of batch processing.

So, batch processing is characterized by the larger quantity of data processed (in the example, a month’s worth of transactions as opposed to a single transaction) and at longer intervals of time (in the example, every month as opposed to as soon as possible) than is customary in stream processing. Another example can be offered by the data ingestions and transformations required as part of an Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) process...

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