How Orchestration Impacts Data Platform Architecture

This episode explores the complexities of data orchestration within modern data platforms. The interview begins by defining orchestration as a method to schedule, trigger, and monitor data processes, highlighting its evolution from simple tools like Cron to sophisticated systems like Kubernetes and Argo CD. Against this backdrop, the discussion pivots to the challenges of managing increasingly complex data workflows, emphasizing the trade-offs between centralized control and federated execution. More significantly, the conversation examines different approaches to gaining visibility into data flows, contrasting the use of metadata catalogs with orchestration engines that incorporate metadata management. For instance, the limitations of Airflow's agnostic approach and the potential drawbacks of centralizing metadata within a single orchestration tool are discussed. The interview concludes by considering the impact of AI and the need for orchestration systems to adapt to the evolving needs of diverse teams, including application engineers, data scientists, and ML/AI specialists, suggesting a future where conversational interfaces might unify these workflows.