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

Rationalizing the costs

At this point, most companies have been building data pipelines for decades, and what initially started as a simple process of transforming and uploading dashboards has now evolved into real data departments with tens, hundreds, and thousands of people working with data. We started by having and maintaining a few pipelines, but today, we have companies with thousands of pipelines that read and write from thousands of different data sources. Therefore, a critical aspect is governing this ecosystem of data pipelines and data stakeholders as well as governing the associated costs. This is especially true when we speak about cloud data architectures based on Software-as-a-Service (SaaS) being available on demand, a kind of provisioning well known for being difficult to measure, control, and predict costs.

Due to this, rationalizing data pipeline costs has become not only important but crucial to guaranteeing the right return on investment and making data analysis...