Orchestrating Python Workflows in Apache Airflow
Formal Metadata
| Title | Orchestrating Python Workflows in Apache Airflow |
|
| Title of Series | |
| Number of Parts | 141 |
| Author | |
| Contributors | |
| License | You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this license. |
| Identifiers | |
| Publisher | |
| Release Date | |
| Language | |
Content Metadata
| Subject Area | |
| Genre | |
| Abstract | [**Apache Airflow**] is an Open Source workflow orchestrator. It is a python library that allows you to automate complex code and integrate it with a plethora of Data Sources. It is provided with an integrated UI and API for both your human and programmatic needs.
After 5 years of running Airflow in production, I hope to share some insights on the technology. The strengths and weaknesses, recommended features and more dangerous ones, and similar considerations on the UI.
I'll also be talking about how **you** can make your own *Operators* in Airflow.
Come take a deeper dive into the same solution used by Airbnb, Slack, Walmart and many more to efficiently run their data pipelines. |
|
