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

Configuring the batch size

To configure the batch size, we will explore how to determine the batch size in Azure Batch. Batch size refers to both the size of Batch pools and the size of the VMs in those pools. The following guidelines are generic enough that they can be applied to other services such as Spark and Hive too.

Here are some of the points to consider while deciding on the batch size:

  • Application requirements: Based on whether the application is CPU-intensive, memory-intensive, storage-intensive, or network-intensive, you will have to choose the right types of VMs and the right sizes. You can find all the supported VM sizes using the following Azure CLI command (here, centralus is an example):
    az batch location list-skus –location centralus
  • Data profile: If you know how your input data is spread, it will help in deciding the VM sizes that will be required. We will have to plan for the highest amount of data that will be processed by each of the VMs.
  • ...