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  • Book Overview & Buying Data Observability for Data Engineering
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Data Observability for Data Engineering

Data Observability for Data Engineering

By : Michele Pinto, Sammy El Khammal
4.7 (3)
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Data Observability for Data Engineering

Data Observability for Data Engineering

4.7 (3)
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

Summary

In this chapter, we addressed some very important issues related to data observability. We focused on learning about the main concepts surrounding data pipelines and how they can be characterized, after which we understood the various types of data pipeline architectures.

Then, we learned how data observability can make a drastic contribution to containing and reducing costs associated with the evolution and maintenance of data pipelines.

After, we analyzed and understood the fundamental role of data lineage and when it is essential to automate the documentation updates, reduce data catalog management, anticipate propagation, mitigate the impacts of a data anomaly, and drastically reduce changing risk.

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Data Observability for Data Engineering
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