
Open Source Airflow Contributions and Performance Improvements at G-Research with Christos Bisias
The Data Flowcast: Mastering Apache Airflow ® for Data Engineering and AI
Modern Airflow isn’t just orchestration. It's a contribution.
In this episode, we explore how open source investment drives real performance gains and deeper observability.
We’re joined by Christos Bisias, Open Source Software Engineer, Apache Airflow at G-Research, to discuss how his team uses Airflow for large-scale data transformations, contributes upstream and improves scheduler throughput and OpenTelemetry support. From trace-level observability to CI-enforced metrics governance and a major scheduler optimization, this conversation spans strategy, engineering and community impact.
Key Takeaways:
00:00 Introduction.
01:20 How G-Research applies machine learning and big data to predict financial market movements.
02:15 Contributing to open source is a business decision.
03:10 Maintaining a fork is costly.
04:30 OpenTelemetry collects metrics, logs and traces to provide deep system visibility.
06:10 Custom spans help identify bottlenecks inside tasks and enable performance optimization.
08:05 OpenTelemetry integration works properly in Airflow 3.0 and above.
10:00 A YAML-based metrics registry with CI enforcement ensures consistency between docs and exported metrics.
12:10 Scheduler throughput improved significantly by applying concurrency limits earlier in the database query.
15:20 Future Task SDK changes may enable language-agnostic DAG authoring beyond Python.
Resources Mentioned:
Christos Bisias
https://www.linkedin.com/in/xbis/
G-Research
https://www.linkedin.com/company/g-research/
Apache Airflow
https://airflow.apache.org/
OpenTelemetry
https://opentelemetry.io/
Prometheus
https://prometheus.io/
Grafana
https://grafana.com/
Jaeger
https://www.jaegertracing.io/
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#AI #Automation #Airflow