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

Implementing a dimensional hierarchy

As we have already explored dimensional hierarchy in Chapter 4, Designing the Serving Layer under the Designing a dimensional hierarchy section we will not be repeating it here. Please have a look at Chapter 4 to refresh your understanding of dimensional hierarchy.

Let's next look at the techniques available for reading and writing data in Parquet files.

Delivering data in Parquet files

In this section, we will learn to deliver data that is present in Parquet files via both Synapse SQL and Spark. We will use the concept of external tables that we learned in the previous chapter to accomplish this.

Let's consider an example where we have the trips data stored as Parquet files. We will use Synapse SQL serverless pools and Spark to access the data and run some sample queries on it.