Related Pages

Related topics: System Architecture and Deployment, Integrations and Extensibility, Operations, Observability, and Community Roadmap

Dagster Overview and Core Concepts

Purpose and Scope

Dagster is an open-source data orchestration framework that enables the declarative definition, execution, and observability of data pipelines. As described in the top-level README.md, Dagster is designed to integrate with popular data tools and deploy to the user's existing infrastructure. The project is Apache 2.0 licensed and the Python package is located at python_modules/dagster.

Dagster's value proposition is twofold: it provides a unified programming model for data assets and their dependencies, and it ships with a web-based UI (Dagit / the Dagster webserver) for inspecting pipeline lineage, running jobs, and observing materialization events. The framework supports both batch and streaming workflows and is intentionally extensible through integrations such as dagster-dbt, dagster-pagerduty, dagster-github, and dagster-papertrail.

Core Concepts

Dagster is built around a small set of first-class abstractions. These abstractions appear consistently across the example projects and form the vocabulary a developer needs to learn.

Assets, Ops, Jobs, and Resources

The most prominent concept is the Software-Defined Asset (often just "asset"). An asset represents a logical data product, such as a table, file, or model artifact, that a function knows how to produce. The examples/quickstart_etl/README.md demonstrates assets like hackernews_topstories and hackernews_topstories_word_cloud, where each function declares its output and Dagster infers the dependency graph. The example project also attaches arbitrary MetadataValue to materializations, which is then displayed in the Asset Details page and Activity tab of the UI.

Ops are the lower-level compute units — individual Python functions that read inputs, perform work, and produce outputs. Ops are composed into Jobs, which are executable graphs of ops or assets. The examples/project_fully_featured/README.md example groups its assets into three logical asset groups — core, recommender, and activity_analytics — that demonstrate how jobs can be organized.

Resources provide pluggable interfaces for external systems (databases, APIs, blob storage). examples/development_to_production/README.md explicitly highlights "swappable resources" as a mechanism for ensuring production data is not overwritten during local development. IOManagers are a related abstraction that govern how asset inputs are read and outputs are written, and they are also referenced in the fully featured project example.

Schedules, Sensors, and Partitions

Dagster provides declarative primitives for triggering pipeline runs. Schedules run jobs on a cron expression; Sensors react to external state changes (for example, the appearance of a new file or the freshness of a downstream dynamic table). The Snowflake Cortex example at examples/snowflake_cortex/dagster_snowflake/README.md describes schedules running daily at 2 AM EST and weekly at Sunday 3 AM EST, along with a sensor that monitors dynamic-table freshness.

Partitions divide an asset into slices (commonly time-based windows). The quickstart ETL partitions the HackerNews fetch by hour, and examples/docs_projects/project_atproto_dashboard/README.md uses *dynamic partitions* together with declarative automation and concurrency limits for the ATProto ingestion pipeline. A recurring community request — issue #17005, "Partitioned asset checks" (69 comments) — asks for checks to be evaluated per-partition rather than per-asset, which would let users validate each slice independently.

Project Layout and Examples

The repository is organized into several top-level directories. examples/README.md describes the example projects as small demonstrations of how Dagster is used in practice; some are flagged UNMAINTAINED in their READMEs, such as examples/assets_modern_data_stack/README.md and examples/project_fully_featured/README.md. Actively maintained, smaller examples include quickstart_etl, assets_dbt_python, development_to_production, and the docs projects under examples/docs_projects/.

Documentation tooling lives in js_modules/dg-docs-components/, which provides React components for both the dg docs standalone site and embedded documentation surfaces inside the Dagster app (js_modules/dg-docs-components/README.md). The python_modules/automation/ directory contains Docker and PyPI publishing tooling (python_modules/automation/README.md).

flowchart LR
    A[Asset / Op Definition] --> B[Job Graph]
    B --> C{Trigger Source}
    C -->|Manual| D[CLI / UI / Python API]
    C -->|Schedule| E[Cron Expression]
    C -->|Sensor| F[External State Change]
    D --> G[Executor]
    E --> G
    F --> G
    G --> H[IOManager / Resource]
    H --> I[(Storage: DB, S3, etc.)]
    G --> J[Dagit Webserver Observability]

Getting Started and Community

The recommended path for new users is the quickstart_etl example. As documented in examples/quickstart_etl/README.md, the user scaffolds the project, installs dependencies with pip install -e ".[dev]", and starts the UI via dagster dev (or, in newer examples such as examples/docs_projects/project_ask_ai_dagster/README.md, dg dev). The UI is served at http://localhost:3000, where the user can materialize assets, view logs, and inspect the lineage graph. The project can also be deployed to Dagster Cloud, the managed offering referenced in examples/assets_dbt_python/README.md.

The latest release at the time of writing is 1.13.10 (core) and 0.29.10 (libraries), which includes a bugfix for DagsterInstance.get_latest_materialization_event returning stale pre-wipe materializations. Longer-running feature requests from the community include Authentication and RBAC in Dagit (#2219, 87 comments), multi-threaded/async executors (#4041, 17 comments), OpenTelemetry trace IDs in log lines (#12353), and a Sqlmesh integration modeled on the existing dbt integration. These issues indicate active community interest in stronger observability, parallel execution, and broader third-party tool coverage.

See Also

• Dagster Assets and Materialization
• Dagster Schedules, Sensors, and Automation
• Dagster Resources and IOManagers
• Dagster Integrations Library

Related Pages

Related topics: Dagster Overview and Core Concepts, Integrations and Extensibility, Operations, Observability, and Community Roadmap

System Architecture and Deployment

Dagster is an open-source data orchestration platform that unifies data pipelines, observability, and asset-centric development into a single framework. As of the 1.13.x release line, Dagster offers a layered deployment topology that ranges from fully local development to managed cloud platforms, giving teams flexibility on where their orchestration code and execution happen. Source: README.md

This page documents the high-level system architecture, the supported deployment topologies, and the runtime components that production Dagster installations rely on.

High-Level Architecture

A Dagster deployment is composed of three logical tiers: the user-defined code containing assets, ops, jobs, schedules, and sensors; the Dagster core services that load and execute that code; and the storage layer that persists run history, event logs, and asset materializations.

flowchart LR
    A[User Code<br/>assets, ops, jobs, schedules] -->|load| B(Dagster Webserver / Dagit)
    A -->|load| C(Dagster Daemon)
    C -->|schedules/sensors| D[Run Coordinator]
    D -->|launch| E[Run Worker / Executor]
    E -->|read/write| F[(Instance Storage<br/>event log, runs, asset catalog)]
    B -->|query| F
    C -->|query| F

The webserver provides the GraphQL API and the React-based UI used to inspect runs and assets. The daemon is a long-running process that ticks schedules and sensors, evaluates declarative automations, and reconciles run state. Both services point at the same DagsterInstance storage backend, so they share a consistent view of the world. Source: python_modules/dagster/README.md

Deployment Topologies

Dagster supports three primary deployment shapes, each described in the official deployment documentation.

In all three topologies, the conceptual components above (webserver, daemon, run workers, storage) exist; what changes is who operates them and where the user code lives. Source: examples/project_multi_tenant/README.md

OSS / Self-Hosted Deployment

An OSS deployment is fully under the operator's control. A workspace file (workspace.yaml) declares one or more code locations, each pointing at a Python module that defines assets and jobs. A typical local OSS workflow looks like:

pip install -e ".[dev]"
dagster dev

The dagster dev command launches both the webserver and the daemon together, pointed at the workspace defined in the current directory. Source: examples/quickstart_etl/README.md

For component-based projects generated with dg, the equivalent command is dg dev, which performs the same role for a components-compatible code location. Source: examples/docs_projects/project_prompt_eng/README.md

Production OSS deployments typically separate the webserver, daemon, and run workers into different processes or containers, all pointing at a shared instance storage backend (Postgres, MySQL, or a cloud equivalent). The fully_featured reference project illustrates this pattern by loading different assets based on the DAGSTER_DEPLOYMENT environment variable (prod, staging, or local). Source: examples/project_fully_featured/README.md

Dagster+ Hybrid

In the Hybrid deployment model, user code stays inside the customer's environment, while Dagster+ provides the control plane: the webserver, the GraphQL API, and the coordination of schedules and sensors. The user maintains the code locations and the run workers that materialize assets, which keeps data resident inside their own network. The multi_tenant example project demonstrates this shape with three independent code locations (harbor_outfitters, summit_financial, beacon_hq) sharing a single workspace configuration. Source: examples/project_multi_tenant/README.md

The project also demonstrates per-location runtime isolation by bundling optional marker packages (catalog_coach_runtime, risk_reviewer_runtime, briefing_writer_runtime) under vendor/ that can be installed into isolated per-location environments. Source: examples/project_multi_tenant/README.md

Dagster+ Serverless

Serverless deployments push the code location into a fully managed build and deploy pipeline. Each push to a Git branch results in a new serverless deployment, and Dagster+ handles scheduling, execution, and storage. The atproto_dashboard reference project is a good example: it combines an ingestion pipeline, a dbt project, and a BI layer, and is designed to be deployed end-to-end on a managed runtime. Source: examples/docs_projects/project_atproto_dashboard/README.md

Core Runtime Components

Code Locations and Workspaces

A code location is a Python package that exports a Definitions object. Multiple code locations are grouped by a workspace.yaml file at the deployment root. In a Dagster+ Hybrid context, code location entries are mirrored by a dagster_cloud.yaml file that adds deployment-specific metadata. Source: examples/project_multi_tenant/README.md

Schedules, Sensors, and Declarative Automation

Long-running orchestration requires a process that can fire schedules and evaluate sensors even when no user is logged in. This is the role of the dagster-daemon service. Daemon-managed automation includes cron-style schedules, polling sensors, and the newer declarative automation policies attached directly to assets. Source: examples/snowflake_cortex/dagster_snowflake/README.md

Execution and Compute Logs

Each run is launched by a run coordinator and executed by an executor inside a worker. The default executor runs steps in subprocesses; community discussion has highlighted that multi-threaded / async execution would require evolving the ComputeLogManager to operate at process scope rather than step scope. Source: community context — #4041

Instance Storage

Every Dagster deployment needs a persistent storage backend for run history, event logs, and asset materialization records. The 1.13.10 release notes for example fixed a bug where DagsterInstance.get_latest_materialization_event could return a stale pre-wipe materialization, illustrating how the storage layer is the system of record for asset state. Source: [community context — release notes 1.13.10]

Local Development Patterns

Quickstart ETL

The quickstart_etl example walks through the canonical local development loop:

1. Install the project with pip install -e ".[dev]".
2. Run dagster dev to start both the webserver and the daemon.
3. Open http://localhost:3000 to inspect the three demo assets (hackernews_topstory_ids, hackernews_topstories, hackernews_stories_word_cloud).
4. Click Reload definition after editing asset code so Dagster picks up the latest changes. Source: examples/quickstart_etl/README.md

Components-Based Projects

Newer projects generated with dg use a components-compatible layout where definitions are assembled from YAML component configuration rather than purely Python code. The migration guide tests in docs_snippets validate that existing projects can be made components-compatible without breaking existing definitions. Source: examples/docs_snippets/docs_snippets_tests/snippet_checks/README.md

Observability and Forward-Looking Concerns

Dagster's webserver exposes per-run trace-like structure visually. The community has requested that those traces be exported as OpenTelemetry spans with trace/span identifiers attached to log lines, so the same execution structure visible in the UI can be piped into external observability platforms. Source: community context — #12353

For multi-user deployments, authentication and role-based access control for Dagit is a long-standing feature request tracked in the issue tracker. Source: community context — #2219

Common Failure Modes and Caveats

• Definition drift. Code edits are only visible after a reload; in production this means restarting or updating the code location. Source: examples/quickstart_etl/README.md
• Experimental APIs. Some examples under examples/experimental use private interfaces that can change without notice. Source: examples/experimental/README.md
• Package naming collisions. Bundled example projects sometimes have naming conflicts with installed packages; documentation in each example notes any required install tweaks. Source: examples/snowflake_cortex/dagster_snowflake/README.md
• Stale materialization reads. The 1.13.10 release fixed a bug where wiping an asset's partitions could leave the wiped materialization reported as the asset's latest; upgrade to at least that release if you rely on get_latest_materialization_event. Source: [community context — release notes 1.13.10]

See Also

• Dagster README
• Quickstart ETL walkthrough
• Multi-tenant reference project
• ATProto dashboard serverless reference
• Components and dg dev workflow
• Docs snippet testing infrastructure

Related Pages

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Operations, Observability, and Community Roadmap

Integrations and Extensibility

Dagster is designed as a "data orchestrator" that connects to a wide ecosystem of external systems — warehouses, transformation tools, ML platforms, LLMs, and bespoke user code — while remaining extensible through a component model and an external-pipeline protocol. The top-level README.md positions Dagster as a unified orchestrator for both scheduled batch jobs and event-driven workflows, and most of the in-repo example projects exist specifically to demonstrate that breadth of integration Source: [README.md].

Library and Example Integrations

Dagster ships a wide catalog of first-party integrations, each maintained as its own library and demonstrated by an example project in examples/. The examples/ directory is the canonical surface for showing how a particular integration is intended to be consumed:

• dbt — demonstrated in the production-style project_fully_featured example, which combines Python assets with dbt models, S3, and Snowflake Source: [examples/project_fully_featured/README.md].
• Snowflake / Snowflake Cortex AI — examples/snowflake_cortex/dagster_snowflake/README.md illustrates multiple phases: structured entity extraction with SNOWFLAKE.CORTEX.COMPLETE, AI_AGG aggregations, dynamic tables as external assets, and environment-driven authentication (password vs. PEM/DER private key) Source: [examples/snowflake_cortex/dagster_snowflake/README.md].
• Anthropic — project_prompt_eng uses Anthropic prompt engineering to look up alternative fuel stations, with uv sync + dg dev as the standard bootstrap path Source: [examples/docs_projects/project_prompt_eng/README.md].
• OpenAI + Pinecone — project_ask_ai_dagster implements a RAG support bot, with the asset lineage rendered in the README showing how ingestion, embedding, and retrieval are stitched together Source: [examples/docs_projects/project_ask_ai_dagster/README.md].
• HackerNews + Pandas — quickstart_etl is the smallest end-to-end reference, fetching from the HackerNews API, transforming with Pandas, and producing a word-cloud asset; it is also the recommended scaffold for Dagster Cloud Serverless onboarding Source: [examples/quickstart_etl/README.md].

The examples/experimental/ directory is reserved for integrations that depend on undocumented or private APIs, with an explicit warning that those APIs "are liable to change at any time without warning" Source: [examples/experimental/README.md].

Components: The Extensibility Surface

For users who want to extend Dagster itself — or wrap their own services as reusable building blocks — the project exposes a components model plus a dedicated React component library for documentation surfaces.

The components layer is exercised by two test fixtures under examples/docs_snippets/docs_snippets_tests/snippet_checks/guides/dg/:

• test_adding_components_to_existing_project/ — demonstrates how an existing code location can be made "components-compatible" by adopting the new scaffolding.
• test_migrating_definitions/ — covers the migration path for definitions that pre-date the components model.

Both fixtures use a shared my-existing-project/README.md sample to drive the doc-snippet tests Source: [examples/docs_snippets/docs_snippets_tests/snippet_checks/guides/dg/test_adding_components_to_existing_project/my-existing-project/README.md, examples/docs_snippets/docs_snippets_tests/snippet_checks/guides/dg/test_migrating_definitions/my-existing-project/README.md].

On the web/UI side, @dagster-io/dg-docs-components is a dedicated package for rendering documentation both inside the Dagster app and as a standalone site. Its package manifest declares a tight dependency footprint: react-markdown, remark-gfm, strip-markdown, highlight.js, and clsx, with peer dependencies on React 18 Source: [js_modules/dg-docs-components/package.json]. The ComponentHeader.tsx source file shows how individual components render Markdown descriptions, support a truncated vs. full description style, and emit Blueprint-style headings — confirming the docs surface is itself built from composable, user-extendable UI primitives Source: [js_modules/dg-docs-components/src/ComponentHeader.tsx, js_modules/dg-docs-components/README.md].

The ui-core package pulls @dagster-io/dg-docs-components in as a workspace dependency, which means user-defined docs components are first-class citizens of the main Dagster UI build Source: [js_modules/ui-core/package.json].

Multi-Tenancy and Cross-Cutting Concerns

Several community-tracked gaps shape the practical limits of the current extensibility story:

The project_multi_tenant example illustrates one pattern for working within those limits: separate code locations (beacon_hq, harbor_outfitters, summit_financial) each pin their own LLM model and runtime marker package, and an embedded backend can be swapped for Ollama via the LLM_BACKEND environment variable Source: [examples/project_multi_tenant/README.md].

Doc-Snippet Testing Pipeline

Because integration examples double as documentation, the repo includes a dedicated snapshot-test pipeline at examples/docs_snippets/docs_snippets_tests/snippet_checks/. It validates that the contents of files in ./docs_snippets/ match the expected output and can regenerate them via tox -e docs_snapshot_update. A DAGSTER_GIT_REPO_DIR environment variable and a make dev_install in the repo root are prerequisites Source: [examples/docs_snippets/docs_snippets_tests/snippet_checks/README.md]. This pipeline is what keeps the integration guides in lockstep with CLI and library output across releases — the latest being 1.13.10 (core) / 0.29.10 (libraries), which fixed stale materialization bugs in DagsterInstance.get_latest_materialization_event and get_asset_records.

See Also

• Dagster README
• python_modules/dagster README
• Examples directory overview
• dg-docs-components package
• Doc snippet testing guide

Related Pages

Related topics: Dagster Overview and Core Concepts, System Architecture and Deployment, Integrations and Extensibility

Operations, Observability, and Community Roadmap

Overview

Dagster is a data orchestration platform that emphasizes software-engineering best practices for data pipelines. The project ships with a core Python package, a web-based UI (Dagit/webserver), and a growing catalog of integrations, examples, and reusable components. Operations, observability, and a community-driven roadmap form three intertwined pillars: users need to *run* their pipelines reliably, *understand* what is happening when they fail or slow down, and *influence* the direction of upstream features.

This page synthesizes the public documentation, example projects, and the most-engaged community issues into a single reference for operators and contributors.

Source: README.md Source: python_modules/dagster/README.md

Operations: Running and Managing Pipelines

Dagster pipelines are organized around assets, jobs, schedules, and sensors, and the project provides multiple deployment patterns depending on scale.

Local and CI-style development

The quickest way to operate Dagster is the dagster dev (or dg dev) command, which launches a local webserver and reloads definitions on code change. The quickstart ETL project demonstrates the standard flow: install with pip install -e ".[dev]", run dagster dev, then navigate to http://localhost:3000 to see the asset lineage and toggle schedules.

Source: examples/quickstart_etl/README.md

The quickstart project defines a daily schedule that can be turned on with a switch in the UI; the README notes that "congratulations, you now have a daily job running in production" once the schedule is enabled. This pattern — define once in code, control at runtime — is the central operational primitive for many users.

Multi-environment and multi-tenant deployments

Larger workloads are demonstrated by the project_fully_featured example, which loads a single codebase into three deployments (production, staging, local) by switching the DAGSTER_DEPLOYMENT environment variable. Production and staging point at S3 and Snowflake under different prefixes; local falls back to the filesystem and DuckDB.

Source: examples/project_fully_featured/README.md

For organizations running isolated business units, project_multi_tenant shows a workspace with multiple code locations (harbor_outfitters, summit_financial, beacon_hq) sharing a common LLM resource, with per-location runtime marker packages and a workspace.yaml that mirrors the Dagster+ multi-location layout. The example ships with an embedded LLM backend so it runs without Docker.

Source: examples/project_multi_tenant/README.md

Graph-backed and composable assets

When a single Python function is not enough, operators compose ops into graphs and wrap the result as a graph-backed asset. The feature_graph_backed_assets example parses airline passenger data by separating each transformation into an op and chaining them, exposing a clean asset boundary while still benefiting from step-level observability.

Source: examples/feature_graph_backed_assets/README.md

Observability: Logs, Health, and Lineage

Observability is delivered through three channels: structured logs, asset lineage visualized in the UI, and reusable documentation components.

Lineage and asset views

Every project in the example catalog ships with a lineage diagram (PNG or SVG) showing the dependency graph between assets. For instance, project_atproto_dashboard shows an end-to-end flow from ATProto ingestion through dbt models to a Power BI dashboard, and explicitly enumerates the Dagster features used (dynamic partitions, declarative automation, concurrency limits).

Source: examples/docs_projects/project_atproto_dashboard/README.md

Reusable docs components

The dg-docs-components package is a small React/TypeScript component library that renders component metadata (name, tags, description) in both the standalone dg docs site and the in-app documentation panel. The header component (ComponentHeader.tsx) supports truncated or full description modes and renders an icon for the component type, illustrating how the same building blocks power both authoring and runtime observability surfaces.

Source: js_modules/dg-docs-components/README.md Source: js_modules/dg-docs-components/src/ComponentHeader.tsx

Integration-level observability

Integration examples, such as the Snowflake Cortex project, expose query-history cost tracking and freshness sensors for dynamic tables, giving operators signals that go beyond simple success/failure. The Snowflake project also includes password and private-key authentication patterns, illustrating that operational concerns (auth, cost, freshness) are first-class design considerations.

Source: examples/snowflake_cortex/dagster_snowflake/README.md

The diagram below summarizes how the operational and observability layers interact with community-driven features.

flowchart LR
    A[Author: code-defined assets, jobs, schedules] --> B[Dagit / webserver UI]
    B --> C[Run history and lineage]
    B --> D[Asset health & freshness]
    A --> E[Compute logs]
    E --> F[External telemetry<br/>e.g. OpenTelemetry]
    B --> G[Alerts and sensors]
    G --> H[PagerDuty / Slack / email]
    subgraph Community Roadmap
        R1[Auth & RBAC]
        R2[Partitioned asset checks]
        R3[Async / multi-threaded executor]
        R4[OpenTelemetry traces]
    end
    R1 -. gap .-> B
    R2 -. gap .-> D
    R3 -. gap .-> A
    R4 -. gap .-> E

Community Roadmap

The most-engaged open issues in the Dagster repository cluster around three operational and observability gaps.

Source: community context from repository issues.

The latest stable release (1.13.10 core / 0.29.10 libraries) shipped a fix for stale materialization events in DagsterInstance.get_latest_materialization_event, which is directly relevant to observability: operators querying "what is the latest run for this asset?" now get post-wipe data instead of ghost records.

Source: community release notes in repository context.

See Also

• examples/quickstart_etl/README.md — minimal end-to-end pipeline.
• examples/project_fully_featured/README.md — multi-environment reference.
• examples/project_multi_tenant/README.md — workspace and code locations.
• js_modules/dg-docs-components/README.md — shared UI components for docs and in-app help.

Doramagic Pitfall Log

Found 13 structured pitfall item(s), including 1 high/blocking item(s). Top priority: Installation risk - Installation risk requires verification.

1. Installation risk: Installation risk requires verification

• Severity: high
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/24989

2. Installation risk: Installation risk requires verification

• Severity: medium
• Finding: Project evidence flags a installation risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/29674

3. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/33946

4. Configuration risk: Configuration risk requires verification

• Severity: medium
• Finding: Project evidence flags a configuration risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/33945

5. Capability evidence risk: Capability evidence risk requires verification

• Severity: medium
• Finding: README/documentation is current enough for a first validation pass.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: capability.assumptions | https://github.com/dagster-io/dagster

6. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/33944

7. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/29693

8. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: community_evidence:github | https://github.com/dagster-io/dagster/issues/33943

9. Maintenance risk: Maintenance risk requires verification

• Severity: medium
• Finding: Project evidence flags a maintenance risk. Review the linked source before relying on this workflow.
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/dagster-io/dagster

10. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: downstream_validation.risk_items | https://github.com/dagster-io/dagster

11. Security or permission risk: Security or permission risk requires verification

• Severity: medium
• Finding: no_demo
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: risks.scoring_risks | https://github.com/dagster-io/dagster

12. Maintenance risk: Maintenance risk requires verification

• Severity: low
• Finding: issue_or_pr_quality=unknown。
• User impact: May increase setup, validation, or first-run risk for the user.
• Recommended check: Reproduce the official install and quickstart path in an isolated environment.
• Evidence: evidence.maintainer_signals | https://github.com/dagster-io/dagster