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Data Ingestion with Python Cookbook

You're reading from   Data Ingestion with Python Cookbook A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837632602
Length 414 pages
Edition 1st Edition
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Author (1):
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Gláucia Esppenchutz Gláucia Esppenchutz
Author Profile Icon Gláucia Esppenchutz
Gláucia Esppenchutz
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Fundamentals of Data Ingestion
2. Chapter 1: Introduction to Data Ingestion FREE CHAPTER 3. Chapter 2: Principals of Data Access – Accessing Your Data 4. Chapter 3: Data Discovery – Understanding Our Data before Ingesting It 5. Chapter 4: Reading CSV and JSON Files and Solving Problems 6. Chapter 5: Ingesting Data from Structured and Unstructured Databases 7. Chapter 6: Using PySpark with Defined and Non-Defined Schemas 8. Chapter 7: Ingesting Analytical Data 9. Part 2: Structuring the Ingestion Pipeline
10. Chapter 8: Designing Monitored Data Workflows 11. Chapter 9: Putting Everything Together with Airflow 12. Chapter 10: Logging and Monitoring Your Data Ingest in Airflow 13. Chapter 11: Automating Your Data Ingestion Pipelines 14. Chapter 12: Using Data Observability for Debugging, Error Handling, and Preventing Downtime 15. Index 16. Other Books You May Enjoy

Using Data Observability for Debugging, Error Handling, and Preventing Downtime

We are reaching the end of our journey through the data ingestion world and have covered many important topics and seen how they could be applied to real-life projects. Now, to finish this book with a flourish, the final topic is the concept of data observability.

Data observability refers to the ability to monitor, understand, and troubleshoot the health, quality, and other vital aspects of data in a big organization or a small project. In summary, it ensures that data is accurate, reliable, and available when needed.

Although each recipe in this chapter can be executed separately, the goal is to configure tools that, when set together, create a monitoring and observability architecture ready to bring value to a project or team.

You will learn about the following recipes:

  • Setting up StatsD for monitoring
  • Setting up Prometheus for storing metrics
  • Setting up Grafana for monitoring...
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