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Table Of Contents
Data Observability for Data Engineering
By :
Data Observability for Data Engineering
By:
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)
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