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

Scaling resources

Scaling refers to the process of increasing or decreasing the compute, storage, or network resources to improve the performance of jobs or reduce expenses. There are two types of scaling: Manual and Automatic. As might be obvious, with manual scaling, we decide on the size beforehand. With automatic scaling, the service dynamically decides on the size of the resources based on various factors, such as the load on the cluster, the cost of running the cluster, time constraints, and more.

Let's explore the scaling options available in Azure Batch and then quickly glance at the options available in Spark and SQL too.

Azure Batch

Azure Batch provides one of the most flexible autoscale options. It lets you specify your own autoscale formula. Azure Batch will then use your formula to decide how many resources to scale up or down to.

A scaling formula can be written based on the following:

  • Time metrics: Using application stats collected at 5-minute...