Building better analytics pipelines is crucial for data-driven organizations as they face increasing complexity and challenges in managing their efforts. The lack of a proper framework and tooling for managing critical data assets makes it difficult to navigate organizational complexity, respond to stakeholder requests, enable self-service on data assets, and collaborate with other data practitioners across the enterprise. An orchestrator platform provides a development process, observability, and performance optimization tools such as scheduling and partitioning, which are essential for scaling data analytics efforts. Data engineers often struggle with managing complex systems, duplicated code, and poorly documented requirements, highlighting the need for an orchestration tool to bring order to the codebase, provide a unified control plane, and simplify the mental model in data analytics. An orchestrator like Dagster brings a unique declarative asset-centric approach that simplifies the mental model, enables selective materialization, and provides features such as schedules, integrations, metadata analysis, partitions, and backfills, allowing data engineers to build with confidence, deploy with ease, and put their data pipelines on autopilot.