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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

Solving scheduling errors

At this point, you may have already experienced some issues with scheduling pipelines not being triggered as expected. If not, don’t worry; it will happen sometime and is totally normal. With several pipelines running in parallel, in different windows, or attached to different timezones, it is expected to be entangled with one or another.

To avoid this entanglement, in this exercise, we will create a diagram to assist in the debugging process, identify the possible causes of a scheduler not working correctly in Airflow, and see how to solve it.

Getting ready

This recipe does not require any technical preparation. Nevertheless, taking notes and writing down the steps we will follow here can be helpful. Writing when learning something new can help to fix the knowledge in our minds, making it easier to remember later.

Back to our exercise; scheduler errors in Airflow typically give the DAG status None, as shown here:

Figure 11.15 – DAG in the Airflow UI with an error in the scheduler
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