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Data Observability for Data Engineering

Data Observability for Data Engineering

By : Michele Pinto, Sammy El Khammal
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Data Observability for Data Engineering

Data Observability for Data Engineering

5 (2)
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)
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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

Defining Rules on Indicators

In the previous chapters, we saw how you could collect events synchronously in your data applications. We also discussed what contextual information you need in order to draw the big picture of what’s happening inside the applications.

Now that you have a lot of contextual information, it is high time to turn it into actionable insights. The metrics you collect during the pipeline execution need to reassure all the stakeholders about the proper execution of the data applications. All the observers of the pipeline need to be informed about how the data pipeline is behaving.

To maintain the trust of data producers and data consumers, we will introduce the concept of expectations, which will define what the engineer needs to achieve in order to keep the pipeline in good shape. These expectations, composed of metrics and rules, will act as sensors to know whether the applications are working as expected or not.

These rules are a key component...

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Data Observability for Data Engineering
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