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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

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781804616024

Length 228 pages

Edition 1st Edition

Languages

Python

Tools

SQL Server

Concepts

Data Engineering

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 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

Why subscribe?

16. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

anomaly detection 128

multiple indicators deterministic cases 129

simple indicator deterministic cases 129

time series analysis 130

anomaly detection case study 130, 131

multiple indicators deterministic cases 133, 134

residuals and anomaly detection 136

seasonality analysis 136

simple indicator deterministic cases 131-133

time series analysis 135, 136

trend analysis 136

application analysis 52

application analyzer method

advantages 56

disadvantages 56

application lineage example 121

data observability method, advantages 128

impact analysis 123

issue, detecting 122

issues, preventing 127

root cause analysis 124-126

troubleshooting 127

application observability 34

application run 75

log 76, 77

applications 72

data...

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Authors (2)

Michele Pinto

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.

See other products by Michele Pinto

Sammy El Khammal

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.

See other products by Sammy El Khammal

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