Dagster

Background Dagster is a modern orchestration and control plane for data and AI pipelines that shifts the mental model from task-by-task workflows to asset-centric data modeling. Instead of wiring together opaque tasks, teams declare the data assets they care about (tables, files, ML models, notebooks) and Dagster manages dependencies, scheduling, and materialization. The platform is available as open source (self-managed) and as a managed offering, Dagster+, which provides a hosted instance and production-focused features and deployment flavors (serverless and hybrid patterns are supported in the product stack). Core capabilities Dagster provides a declarative programming model, integrated lineage, and end-to-end observability. Pipelines and assets are defined in Python using the Dagster SDK and can be scaffolded with the dg CLI. The system includes a browser UI to monitor runs, inspect logs and metadata, and drill into asset states. Key runtime features include partitioned and incremental runs, asset checks and data quality validations, run monitoring with automatic detection/restart options, and a rich API surface (CLIs, GraphQL and REST endpoints) for CI/CD and automation. Testability is a first-class concern: you can unit-test assets and ops, run asset checks locally, and validate partitioned configs before deploying to production. Data quality, lineage, and cost observability Dagster embeds data quality into the pipeline lifecycle rather than treating it as an afterthought. Teams can write native Python validations or integrate third-party tools such as Great Expectations, Soda, dbt tests, Pandera, and other validation libraries. The built-in catalog and lineage view automatically surfaces where datasets come from, which jobs produced them, freshness, and column-level metadata — reducing the need to stitch together separate documentation or metadata systems. Dagster also captures cost and resource metadata for runs (compute time, query usage, Snowflake credits and similar metrics), enabling teams to trace cost back to individual assets, steps, and code to better optimize spend across data and AI workloads. Integrations and extending Dagster Dagster integrates with a wide range of data, ML, and infra tooling so you can orchestrate existing systems rather than rip-and-replace them. First-class and community-supported integrations include dbt, Snowflake, BigQuery, Databricks (PipesDatabricksClient), Spark/PySpark, Kubernetes (PipesK8sClient), Docker (PipesDockerClient), Azure ADLS Gen2, S3/GCS, lakeFS, and many vector DBs and LLM-related services (Chroma, Qdrant, Weaviate, Pinecone, OpenAI, Anthropic, Gemini). There are connectors for observability and alerts (Datadog, Prometheus, PagerDuty), collaboration tooling (Slack, Microsoft Teams), source control automation (GitHub Apps), and MLOps tools (Weights & Biases). Dagster Pipes provides language-agnostic observability for external processes with client implementations in Java, Rust, and TypeScript so non-Python workloads can emit logs, materializations, and metadata back to Dagster. Example use cases - ETL/ELT: Build reproducible ingestion pipelines that read from APIs or files, transform data with Python or Spark, and materialize tables in Snowflake or BigQuery. Use asset checks and freshness rules to prevent stale data from propagating downstream. - ML/AI pipelines: Orchestrate preprocessing, training, and evaluation steps; log model artifacts and metrics; integrate with W&B and track lineage from raw data through model versions. Use cost insights to understand expensive training runs. - Retrieval-Augmented Generation (RAG) & vector workflows: Index documents into Qdrant/Weaviate/Chroma/Pinecone within Dagster assets, run similarity searches as pipeline steps, and attach LLM calls (OpenAI, Anthropic, Gemini) while tracking API usage and credit consumption in Dagster Insights. - Real-time/near-real-time workflows: Implement event-driven systems that detect business signals (e.g., abandoned carts) and invoke marketing or notification platforms, while keeping asset metadata and downstream impacts visible. Getting started and ecosystem You can scaffold a new Dagster project with the dg CLI or add Dagster to an existing Python project. Local development supports rapid iteration and testing before deploying to production. For teams that prefer a managed path, Dagster+ offers a hosted control plane and features designed for production scale; migration paths from OSS to Dagster+ are documented. The project maintains extensive docs, interactive tutorials, a Quickstart to build your first pipeline, public APIs (CLI, SDK, GraphQL, REST), and an active community (Slack, GitHub) for support and discussion. In short, Dagster is positioned as a unifying layer that makes pipelines observable, testable, and cost-aware while integrating with the tools teams already use — letting engineering teams ship data and AI products faster and with more confidence.