Book Image

Data Observability for Data Engineering

By : Michele Pinto, Sammy El Khammal
Book Image

Data Observability for Data Engineering

By: Michele Pinto, Sammy El Khammal

Overview of this book

In the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.
Table of Contents (17 chapters)
1
Part 1: Introduction to Data Observability
4
Part 2: Implementing Data Observability
8
Part 3: How to adopt Data Observability in your organization
12
Part 4: Appendix

Part 4: Appendix

The concluding part of the book provides practical guidance for implementing data observability. It presents a comprehensive checklist method to integrate data observability into company pipelines, highlighting common pitfalls and concerns encountered in various implementations.

The book wraps up with a roadmap for data engineers, offering a technical pathway to effectively implement data observability in an initial project and then scale it across the organization. This final chapter serves as a strategic guide to embedding data observability into the core of data engineering practices.

This part has the following chapters: