Log, Monitor, and Debug Data Pipelines with Python
(https://sanet.pics/storage-11/0325/avif/th_07zMKq4C5m9HaPtWKs737uHaXLVGdiFJ.avif)
Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 130.26 MB | Duration: 50m 10s
Learn to implement structured logging, monitor and debug data pipelines, and enhance observability using tools like Python's logging, Prometheus, Grafana, and Apache Airflow, focusing on improving pipeline reliability in production environments.
Data pipelines can fail due to poor logging, lack of monitoring, and ineffective debugging, leading to unreliable data workflows and operational inefficiencies.
In this course, Log, Monitor, and Debug Data Pipelines with Python, you'll gain the ability to implement structured logging, monitor data pipelines, and effectively troubleshoot failures to ensure smooth operations.
First, you'll explore how to set up structured logging in Python to capture meaningful insights.
Next, you'll discover how to monitor your data pipelines using powerful tools like Prometheus and Grafana.
Finally, you'll learn how to debug common failures, enhance observability with error handling and data lineage, and set up alerts in both cloud and on-premises environments.
When you're finished with this course, you'll have the skills and knowledge needed to log, monitor, and debug data pipelines effectively, ensuring reliability and performance.
Homepage:
https://www.pluralsight.com/courses/python-log-monitor-debug-data-pipelinesScreenshots
(https://sanet.pics/storage-11/0325/avif/th_uTbacafgqAH63hJY24VhLQwfANuRhf8J.avif)
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