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

Creating basic logs in Airflow

The internal Airflow logging library is based on the Python built-in logs, which provide flexible and configurable forms to capture and store log messages using different components of directed acyclic graphs (DAGs). Let’s start this chapter by covering the basic concepts of how Airflow logs work. This knowledge will allow us to apply more advanced concepts and create mature data ingestion pipelines in real-life projects.

In this recipe, we will create a simple DAG to generate logs based on the default configurations of Airflow. We will also understand how Airflow internally sets the logging architecture.

Getting ready

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

Since we will create a new DAG, let’s create a folder under the dag/ directory called basic_logging and a file inside it called basic_logging_dag.py to insert our script. By the end, your folder...