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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
Authors (2):
Michele Pinto Michele Pinto
Profile icon Michele Pinto
Sammy El Khammal Sammy El Khammal
Profile icon Sammy El Khammal
View More author details

Table of Contents (17) Chapters

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

Prerequisites and installation requirements

This chapter introduces many concepts that are used in the kensu-py open source library. If you want to follow how the log file was generated, please refer to the notebook in this book’s GitHub repository, in the chapter 4 section.

If you are familiar with Python and want to run the example by yourself, we advise you to create a virtual environment; see https://docs.python.org/3/library/venv.html for more details.

To install the necessary libraries for this chapter, run pip install –r requirements.txt in this book’s repository’s directory.

Kensu – a data observability framework

In this chapter, we will be working with an open source library called Kensu. It is a Python library that interacts with data transformation libraries to generate observations and send them to the Kensu platform.

Kensu allows you to collect and process data observations through the Kensu platform. You can try out the...

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