Book Image

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
Book Image

Data Ingestion with Python Cookbook

By: Gláucia Esppenchutz

Overview of this book

Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges. You’ll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you’ll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation. By the end of the book, you’ll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.
Table of Contents (17 chapters)
1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

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...