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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

By : Gláucia Esppenchutz
4.5 (4)
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Data Ingestion with Python Cookbook

Data Ingestion with Python Cookbook

4.5 (4)
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)
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1
Part 1: Fundamentals of Data Ingestion
9
Part 2: Structuring the Ingestion Pipeline

Setting up Grafana for monitoring

Grafana is an open source tool built to create visualizations and monitor data from other systems and applications. Together with Prometheus, it is one of the most popular DevOps tools due to its flexibility and rich features.

In this exercise, we will configure a Docker image to run Grafana and connect it to Prometheus. This configuration will not only give us the ability to explore the Airflow metrics even further but also the opportunity to learn in practice how to work with a set of the most popular tools for monitoring and observability.

Getting ready

Refer to the Technical requirements section for this recipe since we will handle it with the same technology.

In this recipe, I will use the same docker-compose.yaml file of Airflow and will keep the configurations from the Setting up StatsD for monitoring and Setting up Prometheus for storing metrics recipes, to connect them and proceed with the monitoring and observability architecture...

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