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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

Setting up the schedule_interval parameter

One of the most widely used parameters in Airflow DAG scheduler configuration is schedule_interval. Together with start_date, it creates a dynamic and continuous trigger for the pipeline. However, there are some small details we need to pay attention to when setting schedule_interval.

This recipe will cover different forms to set up the schedule_interval parameter. We will also explore a practical example to see how the scheduling window works in Airflow, making it more straightforward to manage pipeline executions.

Getting ready

While this exercise does not require any technical preparation, it is recommended to take notes about when the pipeline is supposed to start and the interval between each trigger.

How to do it…

Here, we will show only the default_args dictionary to avoid code redundancy. However, you can always check out the complete code in the GitHub repository: https://github.com/PacktPublishing/Data-Ingestion...

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