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Dagster is a cloud-native data orchestrator designed to help teams build, deploy, and monitor data pipelines efficiently. It provides a structured framework for managing data workflows, ensuring reliability, and enhancing observability.

Key features include:

• Declarative Programming Model: Users define data assets and the functions that produce or update them, promoting clarity and maintainability in workflows.
• Integrated Lineage and Observability: Track the lineage of data assets and monitor pipeline performance with detailed insights, including execution logs and timing.
• Testability: Robust testing capabilities to ensure the reliability of data pipelines through unit tests and validation checks.
• Flexible Deployment Options: Supports fully serverless or hybrid deployments, allowing teams to choose the best architecture for their needs.
• Native Branching: Enables users to create branches in their data pipelines for testing new features without impacting the main workflow.
• Scalability: Designed to scale from individual developers' laptops to large production clusters, supporting both parallel and distributed computing.
• Scheduling and Sensors: Automate pipeline execution based on time schedules or external events, optimizing resource usage and reducing redundant computations.
• Rich API and Integrations: Offers a comprehensive API for integration with other tools and systems, enhancing its utility in diverse data environments.
• Community Support: Backed by an active community with extensive documentation and training resources.

Dagster is suitable for data engineering teams across various industries looking to improve their data management processes, enhance collaboration, and ensure high-quality data delivery.