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Data Observability for Data Engineering

You're reading from Data Observability for Data Engineering

Product type Book

Published in Dec 2023

Publisher Packt

ISBN-13 9781804616024

Pages 228 pages

Edition 1st Edition

Languages

Concepts

Data Processing

Authors (2):

Michele Pinto

Sammy El Khammal

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Introduction to Data Observability

2. Chapter 1: Fundamentals of Data Quality Monitoring

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

Why subscribe?

16. Other Books You May Enjoy

Advanced techniques for data observability – distributed tracing

As we have seen, there are many ways to implement data observability. It is interesting to dedicate some space to analyze how it is possible to track and collect metrics and metadata within a distributed tracing system.

In the last decade, the adoption of architectures with microservices has become more and more common. Developers and DevOps teams have understood that the adoption of complex and monolithic systems is difficult and expensive to maintain as well as not being scalable. However, the adoption of microservices brings some new challenges, such as the ability to monitor and debug transactions distributed between different services.

To comply with the need to observe what happens between distributed transactions, the distributed tracing mechanism was created for monitoring and profiling distributed applications. This is especially useful for debugging anomalies such as errors or performance issues...

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Authors (2)

Michele Pinto

Michele Pinto is the Head of Engineering at Kensu. With over 15 years of experience, Michele has a great knack for understanding how data observability and data engineering are closely linked. He started his career as a software engineer and has worked since then in various roles, such as big data engineer, big data architect, head of data and until recently he was a Head of Engineering. He has a great community presence and believes in giving back to the community. He has also been a teacher for Digital Product Management Master TAG Innovation School in Milan, Italy. His collaboration on the book has been prompt, swift, eager, and very invested.

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See other products by Michele Pinto

Sammy El Khammal

Sammy El Khammal works at Kensu. He started off as a field engineer and worked his way up to the position of product manager. In the past, he has also worked with Mercedes as their Business Development Analyst – Intern. He has also been an O'Reilly teacher for 3 workshops on data quality, lineage monitoring, and data observability. During that time, he provided some brilliant insights, very responsive behaviour, and immense talent and determination.

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