Dagster is a cloud-native data orchestrator designed to streamline the development, deployment, and monitoring of data pipelines. It offers a unified control plane that enables data teams to build, scale, and observe their data and AI workflows with confidence. By modeling data assets such as tables, datasets, machine learning models, and reports, Dagster ensures that these assets are up-to-date and reliable throughout the data lifecycle. Key Features and Functionality: - Data-Aware Orchestration: Dagster models data assets and understands their dependencies, providing full visibility across the data platform. - Integrated Development Environment: Supports local testing, branch deployments, and reusable components, facilitating modern data engineering workflows. - Built-In Data Quality and Observability: Offers tools for data validation, freshness checks, and observability, ensuring data integrity and compliance. - Extensive Integrations: Seamlessly integrates with various tools like dbt, Spark, Snowflake, and more, allowing teams to unify their data stack. - Flexible Deployment Options: Provides both serverless and hybrid deployment models to suit different organizational needs. Primary Value and Problem Solved: Dagster addresses the challenges of managing complex data pipelines by offering a unified, data-aware orchestration platform. It enhances collaboration among data teams, reduces development time, and ensures data quality and compliance. By integrating seamlessly with existing tools and supporting modern software engineering practices, Dagster empowers organizations to build reliable, scalable, and efficient data and AI products.