You're reading from Data Ingestion with Python Cookbook A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process

Product type Paperback

Published in May 2023

Publisher Packt

ISBN-13 9781837632602

Length 414 pages

Edition 1st Edition

Languages

Python

Tools

Apache Airflow

Concepts

Data Engineering

Author (1):

Gláucia Esppenchutz

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Fundamentals of Data Ingestion

2. Chapter 1: Introduction to Data Ingestion FREE CHAPTER

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

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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.