Data Science Orchestration

The discussion highlights the shift from traditional batch job scheduling to a more dynamic and experimental approach in data science. Emphasizing the need for flexibility, the speaker reflects on the limitations of conventional orchestration tools like Airflow, advocating for a solution that accommodates real-time responsiveness and varied data science workflows. A foundational deck from 2017 reveals the essential features envisioned for a data science orchestrator, underscoring the belief that this new tool would complement rather than compete with existing solutions.

In this clip
From this podcast
Open Source Startup Podcast
E10: Prefect - Open-Source Data Flow Automation
Related Questions
- Are there tools to manage workload better?
- How can a browser-based notebook help in data analysis and programming in the context of the episodes Alternatives to Administering and Running Apache Kafka and Python Pandas Tips?

Data Science Orchestration

In this clip

From this podcast

Open Source Startup Podcast

E10: Prefect - Open-Source Data Flow Automation

Related Questions

Are there tools to manage workload better?

How can a browser-based notebook help in data analysis and programming in the context of the episodes Alternatives to Administering and Running Apache Kafka and Python Pandas Tips?