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Data Observability for Data Engineering

You're reading from   Data Observability for Data Engineering Proactive strategies for ensuring data accuracy and addressing broken data pipelines

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804616024
Length 228 pages
Edition 1st Edition
Languages
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Authors (2):
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Michele Pinto Michele Pinto
Author Profile Icon Michele Pinto
Michele Pinto
Sammy El Khammal Sammy El Khammal
Author Profile Icon Sammy El Khammal
Sammy El Khammal
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Introduction to Data Observability
2. Chapter 1: Fundamentals of Data Quality Monitoring FREE CHAPTER 3. Chapter 2: Fundamentals of Data Observability 4. Part 2: Implementing Data Observability
5. Chapter 3: Data Observability Techniques 6. Chapter 4: Data Observability Elements 7. Chapter 5: Defining Rules on Indicators 8. Part 3: How to adopt Data Observability in your organization
9. Chapter 6: Root Cause Analysis 10. Chapter 7: Optimizing Data Pipelines 11. Chapter 8: Organizing Data Teams and Measuring the Success of Data Observability 12. Part 4: Appendix
13. Chapter 9: Data Observability Checklist 14. Chapter 10: Pathway to Data Observability 15. Index 16. Other Books You May Enjoy

Analyzing the application

A common way to understand what happens in an application is to replay its course after it’s run. A good example would be a SQL application. When you query a SQL database, for instance, through a JDBC connector, you are creating access logs in the database. These logs may contain lots of information, especially regarding who has queried the database, what they queried, when it was executed, and sometimes information on how long it took to process the query, how many bytes were retrieved, and so on.

This situation is explained in Figure 3.4. Users are continuously querying a central SQL database. This creates a log file, which is a kind of journal that contains the records of the queries:

Figure 3.4 – Logging strategy for a SQL logs analyzer

Figure 3.4 – Logging strategy for a SQL logs analyzer

This said, these logs can be extremely valuable for observability purposes. By using strategies to retrieve and analyze the logs, the data team can rebuild data transformation...

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