Book Image

Azure Data Engineer Associate Certification Guide

By : Newton Alex
Book Image

Azure Data Engineer Associate Certification Guide

By: Newton Alex

Overview of this book

Azure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.
Table of Contents (23 chapters)
1
Part 1: Azure Basics
3
Part 2: Data Storage
10
Part 3: Design and Develop Data Processing (25-30%)
15
Part 4: Design and Implement Data Security (10-15%)
17
Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)
20
Part 6: Practice Exercises

Managing Spark jobs in a pipeline

Managing Spark jobs in a pipeline involves two aspects:

  • Managing the attributes of the pipeline's runtime that launches the Spark activity: Managing the Spark activity pipeline attributes is no different than managing any other activities in a pipeline. The Managing and Monitoring pages we saw in Figure 11.9, Figure 11.11, and Figure 11.12 are the same for any Spark activity as well. You can use the options provided on these screens to manage your Spark activity.
  • Managing Spark jobs and configurations: This involves understanding how Spark works, being able to tune the jobs, and so on. We have a complete chapter dedicated to optimizing Synapse SQL and Spark jobs towards the end of this book. You can refer to Chapter 14, Optimizing and Troubleshooting Data Storage and Data Processing, to learn more about managing and tuning Spark jobs.

In this section, we'll learn how to add an Apache Spark job (via HDInsight) to our pipeline...