Data Tech 2026

As data platforms scale, managing deployments across multiple teams and parallel feature development becomes increasingly complex in modern cloud platforms. Azure Data Factory (ADF), while widely used for data orchestration, relies on a full-state deployment model—forcing entire pipeline states to be promoted across environments. This creates a critical challenge: how to safely release only intended changes without impacting in-progress work.

In this session, we present a practical approach to enabling selective deployments in ADF using a manifest-driven packaging pattern integrated into CI/CD workflows. By introducing dependency-aware subset generation, teams can deploy only required pipelines while safely managing related datasets, triggers, and linked services.

We will cover real-world implementation patterns, including validation strategies, incremental deployment, and separation of application and infrastructure concerns. The approach has been applied in enterprise environments where data reliability, governance, and release independence are critical.

Attendees will gain actionable insights and reusable patterns to improve deployment safety, reduce operational risk, and enable intent-driven delivery in modern data platforms.