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Data Ingestion with Python Cookbook

You're reading from  Data Ingestion with Python Cookbook

Product type Book
Published in May 2023
Publisher Packt
ISBN-13 9781837632602
Pages 414 pages
Edition 1st Edition
Languages
Author (1):
Gláucia Esppenchutz Gláucia Esppenchutz
Profile icon Gláucia Esppenchutz

Table of Contents (17) Chapters

Preface 1. Part 1: Fundamentals of Data Ingestion
2. Chapter 1: Introduction to Data Ingestion 3. Chapter 2: Principals of Data Access – Accessing Your Data 4. Chapter 3: Data Discovery – Understanding Our Data before Ingesting It 5. Chapter 4: Reading CSV and JSON Files and Solving Problems 6. Chapter 5: Ingesting Data from Structured and Unstructured Databases 7. Chapter 6: Using PySpark with Defined and Non-Defined Schemas 8. Chapter 7: Ingesting Analytical Data 9. Part 2: Structuring the Ingestion Pipeline
10. Chapter 8: Designing Monitored Data Workflows 11. Chapter 9: Putting Everything Together with Airflow 12. Chapter 10: Logging and Monitoring Your Data Ingest in Airflow 13. Chapter 11: Automating Your Data Ingestion Pipelines 14. Chapter 12: Using Data Observability for Debugging, Error Handling, and Preventing Downtime 15. Index 16. Other Books You May Enjoy

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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