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Azure Data Engineer Associate Certification Guide

You're reading from   Azure Data Engineer Associate Certification Guide Ace the DP-203 exam with advanced data engineering skills

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781805124689
Length 548 pages
Edition 2nd Edition
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Authors (3):
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Newton Alex Newton Alex
Author Profile Icon Newton Alex
Newton Alex
Giacinto Palmieri Giacinto Palmieri
Author Profile Icon Giacinto Palmieri
Giacinto Palmieri
Mr. Surendra Mettapalli Mr. Surendra Mettapalli
Author Profile Icon Mr. Surendra Mettapalli
Mr. Surendra Mettapalli
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Azure Basics FREE CHAPTER
2. Chapter 1: Introducing Azure Basics 3. Part 2: Data Storage
4. Chapter 2: Implementing a Partition Strategy 5. Chapter 3: Designing and Implementing the Data Exploration Layer 6. Part 3:Data Processing
7. Chapter 4: Ingesting and Transforming Data 8. Chapter 5: Developing a Batch Processing Solution 9. Chapter 6: Developing a Stream Processing Solution 10. Chapter 7: Managing Batches and Pipelines 11. Part 4:Secure, Monitor, and Optimize Data Storage and Processing
12. Chapter 8: Implementing Data Security 13. Chapter 9: Monitoring Data Storage and Data Processing 14. Chapter 10: Optimizing and Troubleshooting Data Storage and Data Processing 15. Chapter 11: Accessing the Online Practice Resources 16. Other Books You May Enjoy

Summary

In this chapter, you explored several new concepts, some of which may require considerable time and effort to fully grasp. Tasks such as handling skew in data, data spill, tuning queries, and troubleshooting failed pipelines and jobs are complex enough to warrant their own books. An overview of these topics was provided, along with additional resources for further exploration.

You learned about essential concepts for efficient Big Data Analytics, by addressing the issue of small files, as well as data compaction techniques to improve storage efficiency and query performance. After that, you explored strategies for handling data skew and spills, which is crucial for optimizing SQL and Spark environments, and then examined shuffle partitions in Spark, where techniques such as indexing and caching for performance enhancement were discussed. Additionally, you saw general tips for resource management and guidelines for debugging Spark jobs.

By now, you should have a solid...

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