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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
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Newton Alex
Giacinto Palmieri Giacinto Palmieri
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Giacinto Palmieri
Mr. Surendra Mettapalli Mr. Surendra Mettapalli
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Mr. Surendra Mettapalli
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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

Partition Strategy for Efficiency and Performance

In the Benefits of Partitioning section, you learned how partitioning helps with performance, scale, security, availability, and so on.

The following are some strategies to be kept in mind while designing for efficiency and performance:

  • Partition datasets into smaller chunks that can be run with optimal parallelism for multiple queries.
  • Partition the data so that queries don’t end up requiring too much data from other partitions—that is, minimize cross-partition data transfers.
  • Design effective folder structures to improve the efficiency of data reads and writes. The following is the sample directory structure that you can create within ADLS Gen2 (Figure 2.4):
Figure 2.4 - An organizational diagram illustrates the hierarchical directory structure within Azure Data Lake Storage Gen2 for sales data. At the top level is “Sales data,” which branches into four subdirectories: “Raw data” with a nested “Source files” subdirectory, “Staging area,” “Processed data” and “Reports.” The structure is depicted with clear lines indicating the relationship and flow between each directory level.

Figure 2.4 – The directory structure within ADLS Gen2

  • Use descriptive names for files, reflecting their content, date, or purpose; for example, “sales_data_2024_q1.csv.” Also consider...
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