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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

Developing a Stream Processing Solution

Building on the foundation of Spark, Azure Data Factory (ADF), and Synapse pipelines, Chapter 5, Developing a Batch Processing Solution, equipped you to effectively process large amounts of data.

In this chapter, you will cover another data method, stream processing solutions, also known as real-time processing systems. Similar to batch processing, stream processing is another important segment of data pipelines. This chapter will focus on introducing the concepts and technologies involved in building a stream processing system. You will be learning about technologies such as Azure Stream Analytics (ASA), Azure Event Hubs, and Spark (from a streaming perspective).

You will learn how to build end-to-end streaming solutions using these technologies and explore important streaming concepts such as checkpointing, windowed aggregates, replaying older stream data, handling drift, and stream management concepts such as distributing streams across...

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