Airflow: To Manage Data Pipelines
A significant part of the IT/Data Engineering team is spent on writing and scheduling jobs, monitoring and troubleshooting the issues. Enterprise data originates from various sources and there are various business rules and processes that govern how that data can be consumed.
Airflow is a platform to programmatically author, schedule and monitor workflows. (https://airflow.incubator.apache.org/)
The various tasks in the workflow(s) are configured as a Directed Acyclic Graph. This talk covers how Airflow is used to establish better workflows for data engineering.
P.S: This talk is inspired from Bargava Subramanian (@barsubra) proposal.
- Introduction to Airflow
- Existing challenges in data engineering - creating/monitoring/troubleshooting workflows
- Main advantages of Airflow
- Basic Concepts
- Airflow in practice - case study
Mahendra Yadav is a Data Engineer at Azri Solutions, Hyderabad. In his day to day work he processes a lot of data from different sources.
- Github: https://github.com/userimack
- Blog: https://userimack.wordpress.com/
- Documentation: https://airflow.incubator.apache.org/
- Github: https://github.com/apache/incubator-airflow