You're reading from Azure Data Engineer Associate Certification Guide A hands-on reference guide to developing your data engineering skills and preparing for the DP-203 exam

Product type Paperback

Published in Feb 2022

Publisher Packt

ISBN-13 9781801816069

Length 574 pages

Edition 1st Edition

Tools

Azure

Concepts

Big Data

Author (1):

Newton Alex

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1: Azure Basics

2. Chapter 1: Introducing Azure Basics FREE CHAPTER

3. Part 2: Data Storage

4. Chapter 2: Designing a Data Storage Structure

5. Chapter 3: Designing a Partition Strategy

6. Chapter 4: Designing the Serving Layer

7. Chapter 5: Implementing Physical Data Storage Structures

8. Chapter 6: Implementing Logical Data Structures

9. Chapter 7: Implementing the Serving Layer

10. Part 3: Design and Develop Data Processing (25-30%)

11. Chapter 8: Ingesting and Transforming Data

12. Chapter 9: Designing and Developing a Batch Processing Solution

13. Chapter 10: Designing and Developing a Stream Processing Solution

14. Chapter 11: Managing Batches and Pipelines

15. Part 4: Design and Implement Data Security (10-15%)

16. Chapter 12: Designing Security for Data Policies and Standards

17. Part 5: Monitor and Optimize Data Storage and Data Processing (10-15%)

18. Chapter 13: Monitoring Data Storage and Data Processing

19. Chapter 14: Optimizing and Troubleshooting Data Storage and Data Processing

20. Part 6: Practice Exercises

21. Chapter 15: Sample Questions with Solutions

22. Other Books You May Enjoy

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: