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Azure Data Engineer Associate Certification Guide

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

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Product type Paperback
Published in Feb 2022
Publisher Packt
ISBN-13 9781801816069
Length 574 pages
Edition 1st Edition
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Author (1):
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Newton Alex Newton Alex
Author Profile Icon Newton Alex
Newton Alex
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Toc

Table of Contents (23) Chapters Close

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

Summary

This chapter introduced a lot of new technologies and techniques, and I hope you got a grasp of them. Even though the number of technologies involved is high, the weightage of this chapter with respect to the certification is relatively low, so you may have noticed that I've kept the topics slightly at a higher level and have provided further links for you to read more on the topics.

In this chapter, we started by introducing Azure Monitor and Log Analytics. We learned how to send log data to Log Analytics, how to define custom logging options, and how to interpret metrics and logs data. After that, we focused on measuring the performance of data movements, pipelines, SQL queries, and Spark queries. We also learned how to interpret Spark DAGs, before moving on to monitoring cluster performance and cluster pipeline tests. You should now be able to set up a monitoring solution for your data pipelines and be able to tell if your data movement, pipeline setups, cluster...

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