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

Monitoring cluster performance

Since services such as Synapse and ADF are platform-as-a-service (PaaS) services where you will not have explicit control over the clusters, the one place where we can control each and every aspect of a cluster is the Azure HDInsight service. In HDInsight, you can create your own Hadoop, Spark, Hive, HBase, and other clusters and control every aspect of the cluster. You can use Log Analytics to monitor cluster performance, as with the other examples we saw earlier in the chapter. You can learn more about using Log Analytics in HDInsight here: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-oms-log-analytics-tutorial.

Apart from the Log Analytics approach, there are four main areas of the HDInsight portal that help monitor cluster performance. Let's look at them.

Monitoring overall cluster performance

The HDInsight Ambari dashboard is the first place to check for cluster health. If you see very high heap usage, disk usage...