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

Storing log files in a remote location

By default, Airflow stores and organizes its logs in a local folder with easy access for developers, which facilitates the debugging process when something does not go as expected. However, working with larger projects or teams makes giving everyone access to an Airflow instance or server almost impracticable. Besides looking at the DAG console output, there are other ways to allow access to the logging folder without granting access to Airflow’s server.

One of the most straightforward solutions is to export logs to external storage, such as S3 or Google Cloud Storage. The good news is that Airflow already has native support to export records to cloud resources.

In this recipe, we will set a configuration in our airflow.cfg file that allows the use of the remote logging feature and test it using an example DAG.

Getting ready

Refer to the Technical requirements section for this recipe.

AWS S3

To complete this exercise,...