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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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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

Microsoft Purview

Microsoft Purview is the most recently added service covered by the DP-203 exam. Its focus is on data documentation, particularly to verify and enforce regulatory compliance.

Note

This section primarily focuses on the Push new or updated data lineage to Microsoft Purview concept of the DP-203: Data Engineering on Microsoft Azure exam.

Even if you are not interested or involved in compliance, having a centralized place to collect and access information about your data services can offer other important advantages. A particularly important one is the ability to track data lineage – “what depends on what.” For instance, you might have developed a Data Factory pipeline that “copies” files from a data lake, which then calls a Spark notebook that creates “staging tables” in a Synapse Analytics dedicated SQL pool, which then calls a data flow that calculates aggregations and stores them in another dedicated SQL pool...

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