Operationalizing Data Orchestration: Best Practices for DevOps, Infra, and Code Locations

The comprehensive guide explores the intricacies of operating data orchestration layers within open data platforms, emphasizing the significance of integrating DevOps and GitOps practices for efficient deployment and management. It discusses the challenges of scaling data engineering, especially in the context of AI and generative AI, where data use intensifies, highlighting the role of GitOps in automating infrastructure changes through code repositories. The guide delves into best practices for deploying data orchestration tools like Dagster, focusing on separating business logic from technical infrastructure and emphasizing the need for partitioned, incremental processing to optimize costs. It addresses the importance of governance and testing in ensuring data quality and reliability, advocating for the use of code locations to manage stateful and stateless processes effectively. Additionally, it highlights the need for a multi-tenancy approach to support various teams' autonomy while maintaining a unified data platform strategy, ultimately positioning orchestration as the operating system of a modern data platform that integrates diverse tools and functions seamlessly.