{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dagster","slug":"dagster","name":"Dagster","type":"framework","url":"https://github.com/dagster-io/dagster","page_url":"https://unfragile.ai/dagster","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dagster__cap_0","uri":"capability://automation.workflow.software.defined.asset.graph.with.declarative.dependencies","name":"software-defined asset graph with declarative dependencies","description":"Dagster's core asset system uses Python decorators (@asset) to define data assets as first-class objects with explicit dependency graphs. Unlike traditional DAGs that model tasks, Dagster's asset-centric model tracks data lineage and materialization state directly. The system builds a directed acyclic graph of asset dependencies at definition time, enabling automatic scheduling, backfilling, and impact analysis across the entire data lineage.","intents":["Define data pipelines where assets (tables, models, reports) are the primary abstraction rather than tasks","Automatically determine which downstream assets need re-materialization when an upstream asset changes","Visualize and understand data lineage and dependencies across the entire organization","Backfill historical data for specific assets without re-running unrelated computations"],"best_for":["Data teams building analytics and ML pipelines who want asset-centric orchestration","Organizations migrating from Airflow who need clearer data lineage tracking","Teams requiring fine-grained control over which assets to materialize and when"],"limitations":["Asset definitions are Python-only; no YAML-based asset configuration without custom loaders","Dynamic asset creation at runtime requires AssetSelection patterns; not as flexible as pure task-based DAGs for highly variable workloads","Asset partitioning adds complexity; requires understanding of partition keys and dimension hierarchies"],"requires":["Python 3.8+","Dagster core library (pip install dagster)","Understanding of Python decorators and function signatures"],"input_types":["Python functions with @asset decorator","Asset dependencies via function parameters","Partition definitions and asset selection expressions"],"output_types":["Asset graph (JSON/GraphQL queryable)","Materialization events with asset keys and versions","Lineage metadata for downstream impact analysis"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_1","uri":"capability://data.processing.analysis.type.checked.i.o.with.custom.i.o.managers","name":"type-checked i/o with custom i/o managers","description":"Dagster implements a pluggable I/O manager system that handles serialization, deserialization, and storage of asset outputs with full type checking. Each asset can declare input/output types (Python type hints), and the framework validates data at materialization time. I/O managers are resource-based, allowing different storage backends (S3, Snowflake, local filesystem, etc.) to be swapped without changing asset definitions. The system supports both in-memory and persistent storage with automatic schema validation.","intents":["Ensure type safety across asset boundaries without manual serialization code","Switch storage backends (local → S3 → Snowflake) by changing resource configuration, not asset code","Validate data schemas at materialization time to catch data quality issues early","Handle complex types (DataFrames, Pydantic models, custom objects) with automatic serialization"],"best_for":["Teams building type-safe data pipelines with strict schema contracts","Organizations using multiple storage backends and needing abstraction over I/O","Data platforms requiring schema validation and data quality checks at asset boundaries"],"limitations":["Custom I/O managers require implementing DagsterTypeLoaderContext interface; boilerplate for simple cases","Type checking is runtime-based, not compile-time; Python's duck typing limits static guarantees","Large object serialization can be slow; no built-in compression or streaming for multi-GB assets","I/O manager selection is per-asset; no automatic routing based on asset type or size"],"requires":["Python 3.8+ with type hints support","Custom I/O manager implementation for non-standard storage backends","Understanding of Dagster's resource and context system"],"input_types":["Python type hints (int, str, pd.DataFrame, Pydantic models, custom classes)","I/O manager resource definitions","Asset output metadata and tags"],"output_types":["Serialized data in configured storage backend","Type validation events and schema metadata","Materialization records with type information"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_10","uri":"capability://safety.moderation.asset.health.tracking.and.freshness.monitoring","name":"asset health tracking and freshness monitoring","description":"Dagster's asset health system tracks the freshness and status of assets based on materialization time and custom health checks. The system supports freshness policies (e.g., 'must be materialized daily') that are evaluated by the asset daemon, triggering re-materialization if assets become stale. Custom health checks can be defined as Python functions that assess asset quality (row counts, schema validation, etc.). Asset health status is persisted and queryable via GraphQL, enabling monitoring dashboards and alerting. The system integrates with dbt test results for test-based health tracking.","intents":["Monitor asset freshness and automatically trigger re-materialization if assets become stale","Define custom health checks to validate asset quality (row counts, schema, data ranges)","Track asset health status over time for SLA monitoring and reporting","Integrate dbt test results as asset health indicators"],"best_for":["Data teams requiring SLA monitoring and freshness guarantees","Organizations with data quality requirements and automated validation","Teams building data observability platforms with health dashboards"],"limitations":["Freshness policies are evaluated by asset daemon; no real-time freshness tracking","Custom health checks are synchronous; slow checks can block asset daemon","Health check failures don't automatically trigger remediation; requires separate automation","No built-in alerting; requires external monitoring system integration"],"requires":["Asset daemon running (dagster-daemon process)","Freshness policy definitions on assets","Custom health check implementations (optional)"],"input_types":["Freshness policy specifications (max_age, cron expressions)","Custom health check functions","Asset materialization events"],"output_types":["Asset health status (healthy, stale, unhealthy)","Freshness policy evaluation results","Health check execution events"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_11","uri":"capability://automation.workflow.multi.run.execution.with.dynamic.partitioning.and.backfill.orchestration","name":"multi-run execution with dynamic partitioning and backfill orchestration","description":"Dagster's execution engine supports launching multiple runs for different asset partitions in parallel, with automatic partition key mapping across dependencies. The backfill system enables selecting specific asset partitions and automatically generating run requests for all affected downstream assets. The system tracks backfill progress and supports cancellation/resumption. Execution can be distributed across multiple workers using executors (in-process, multiprocess, Kubernetes, Celery), with automatic work distribution and resource management.","intents":["Backfill historical data for specific date ranges or tenant subsets","Execute asset partitions in parallel across multiple workers","Automatically determine which downstream assets need re-materialization during backfill","Monitor backfill progress and handle failures with automatic retry"],"best_for":["Data teams processing large historical datasets requiring selective backfilling","Organizations with multi-tenant data requiring per-tenant backfills","Teams needing distributed execution across multiple machines"],"limitations":["Backfill performance depends on partition count and executor configuration; can be slow for large backfills","Executor configuration is complex; requires understanding of resource limits and worker allocation","Distributed execution adds operational complexity; requires managing worker infrastructure","Backfill cancellation is not atomic; may leave partial results"],"requires":["Asset definitions with partition specifications","Executor configuration (in-process, multiprocess, Kubernetes, etc.)","Worker infrastructure for distributed execution (optional)"],"input_types":["Backfill request with asset selection and partition key ranges","Executor configuration","Resource specifications for workers"],"output_types":["Run requests for selected asset partitions","Backfill progress tracking","Execution results with status per partition"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_12","uri":"capability://automation.workflow.dagster.cloud.deployment.with.managed.infrastructure","name":"dagster+ cloud deployment with managed infrastructure","description":"Dagster+ is a managed cloud service offering that provides hosted Dagster instances with built-in infrastructure, monitoring, and team collaboration features. It includes managed code locations (serverless execution), automatic scaling, integrated monitoring dashboards, and RBAC for team access control. Dagster+ abstracts away infrastructure management (Kubernetes, databases, etc.), enabling teams to focus on pipeline development. The service supports multiple deployment options (single-tenant, multi-tenant) and integrates with cloud providers (AWS, GCP, Azure).","intents":["Deploy Dagster pipelines to managed cloud infrastructure without managing Kubernetes/databases","Enable team collaboration with RBAC and workspace management","Monitor pipeline execution with built-in dashboards and alerting","Scale pipelines automatically based on workload"],"best_for":["Teams wanting managed Dagster without infrastructure overhead","Organizations requiring team collaboration and RBAC","Companies needing cloud-native deployment with automatic scaling"],"limitations":["Dagster+ is a paid service; no free tier for production use","Vendor lock-in to Dagster's cloud platform; migration to self-hosted requires effort","Limited customization of underlying infrastructure; no direct Kubernetes access","Data residency requirements may not be met in all regions"],"requires":["Dagster+ subscription","Cloud provider account (AWS, GCP, Azure)","Code location configuration for managed execution"],"input_types":["Code location specifications","Deployment configuration","Team and RBAC settings"],"output_types":["Managed Dagster instance","Execution logs and monitoring data","Team access tokens and credentials"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_13","uri":"capability://data.processing.analysis.metadata.and.tagging.system.for.asset.governance","name":"metadata and tagging system for asset governance","description":"Dagster's metadata system enables attaching arbitrary key-value metadata to assets, runs, and events for governance and discovery. Assets can be tagged with custom tags (owner, domain, sensitivity level) that are queryable and filterable. Metadata can include descriptions, SLAs, data quality thresholds, and custom domain-specific information. The system supports metadata inference from external sources (dbt tags, database schemas) and enables metadata-driven automation (e.g., triggering different actions based on asset tags). Metadata is persisted and queryable via GraphQL.","intents":["Tag assets with ownership, domain, and sensitivity information for governance","Attach SLAs and data quality thresholds to assets for monitoring","Enable metadata-driven automation (e.g., different handling for PII assets)","Discover and filter assets based on custom metadata"],"best_for":["Data governance teams requiring asset classification and ownership tracking","Organizations with data sensitivity requirements (PII, regulated data)","Teams building metadata-driven data platforms"],"limitations":["Metadata is unstructured; no schema validation or type checking","Metadata-driven automation requires custom code; no declarative rules engine","Metadata inference from external sources requires custom integration code","No built-in metadata versioning; changes overwrite previous values"],"requires":["Asset definitions with metadata specifications","Custom metadata keys and value formats","Metadata-driven automation code (optional)"],"input_types":["Metadata key-value pairs","Asset tags and descriptions","Custom metadata objects"],"output_types":["Persisted metadata in Dagster instance","Queryable metadata via GraphQL","Metadata-driven automation triggers"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_2","uri":"capability://automation.workflow.declarative.automation.with.sensors.and.dynamic.scheduling","name":"declarative automation with sensors and dynamic scheduling","description":"Dagster's automation layer uses sensors (event-driven triggers) and schedules (time-based triggers) to declaratively define when assets should materialize. Sensors poll external systems (S3, databases, APIs) or listen to Dagster events, while schedules use cron expressions or custom tick functions. The asset daemon continuously evaluates sensor/schedule conditions and creates runs when triggered. Dynamic partitions allow sensors to create new partitions at runtime based on external data (e.g., new S3 prefixes), enabling adaptive pipelines that scale with data growth.","intents":["Trigger asset materialization when upstream data arrives (S3 file drops, database updates)","Schedule assets on fixed intervals (hourly, daily) with timezone-aware cron expressions","Dynamically create asset partitions based on external data without manual configuration","Implement backpressure and retry logic for failed sensor evaluations"],"best_for":["Data teams with event-driven pipelines triggered by external data arrivals","Organizations with multi-tenant or dynamic data structures requiring adaptive partitioning","Teams needing fine-grained control over scheduling logic beyond simple cron"],"limitations":["Sensor polling adds latency; no true push-based event subscriptions (polling interval typically 30-60s)","Dynamic partitions require careful handling of partition key generation; can create runaway partition explosion if not bounded","Sensor state is stored in Dagster instance; no distributed state management for multi-daemon setups","Sensor evaluation is single-threaded per daemon; high-frequency sensors can block other automation"],"requires":["Dagster instance with asset daemon running (dagster-daemon process)","Sensor or schedule definitions using @sensor or @schedule decorators","External system credentials (AWS, database connections) for event polling"],"input_types":["Sensor context with access to Dagster events and external APIs","Schedule tick context with execution timestamp","Dynamic partition request objects with partition key specifications"],"output_types":["Sensor/schedule cursor state (persisted for resumption)","Run requests with asset selection and partition keys","Dynamic partition definitions with keys and tags"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_3","uri":"capability://data.processing.analysis.asset.partitioning.with.multi.dimensional.partition.spaces","name":"asset partitioning with multi-dimensional partition spaces","description":"Dagster's partitioning system enables dividing assets into logical chunks (daily, hourly, by tenant, by region) with support for multi-dimensional partition spaces. Partition definitions are declarative objects (DailyPartitionsDefinition, StaticPartitionsDefinition, DynamicPartitionsDefinition) that define the partition key space. Assets can depend on specific partitions of upstream assets, and the system automatically maps partition keys through the dependency graph. Backfills operate at partition granularity, allowing selective re-materialization of historical data without full asset re-runs.","intents":["Divide large datasets into time-based partitions (daily, hourly) for incremental processing","Create multi-dimensional partitions (date × tenant × region) for complex data structures","Backfill specific date ranges or tenant subsets without re-processing entire datasets","Enable parallel execution of independent partitions across multiple workers"],"best_for":["Data teams processing large time-series datasets requiring incremental updates","Multi-tenant SaaS platforms needing per-tenant data isolation and backfilling","Organizations with complex partition hierarchies (e.g., date + geography + product)"],"limitations":["Partition key mapping across assets requires explicit configuration; implicit mapping can be error-prone","Dynamic partitions can explode in cardinality; no built-in safeguards against runaway partition creation","Backfill performance degrades with high partition counts (>10k partitions); requires careful partition granularity design","Multi-dimensional partitions require understanding of partition space Cartesian products; can be complex to reason about"],"requires":["Partition definition objects (DailyPartitionsDefinition, etc.)","Asset definitions with partition_key_range parameter","Understanding of partition key formats and mapping logic"],"input_types":["Partition definition specifications (start_date, end_date, partition_fn)","Asset dependency partition mappings","Backfill request with partition key ranges"],"output_types":["Partition-specific asset materializations","Partition status tracking (materialized, missing, failed)","Backfill run requests with partition subsets"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_4","uri":"capability://data.processing.analysis.dbt.integration.with.asset.lineage.synchronization","name":"dbt integration with asset lineage synchronization","description":"Dagster's dbt integration (via dagster-dbt library) automatically ingests dbt projects and materializes dbt models as Dagster assets with full lineage preservation. The system parses dbt manifests to extract model dependencies, tags, and metadata, creating asset definitions without manual code. Dagster can orchestrate dbt runs (dbt run, dbt test) as asset materializations, track dbt test results as asset health indicators, and integrate dbt lineage with non-dbt assets in the same graph. The integration supports both local dbt projects and dbt Cloud APIs.","intents":["Automatically convert dbt models into Dagster assets with preserved lineage and dependencies","Orchestrate dbt runs as part of larger Dagster pipelines alongside Python assets","Track dbt test results and model freshness as asset health metrics","Integrate dbt lineage with upstream data ingestion and downstream analytics assets"],"best_for":["Analytics teams using dbt who want unified orchestration with Dagster","Organizations with hybrid dbt + Python pipelines requiring integrated lineage","Teams migrating from dbt-only orchestration to Dagster for end-to-end data platform"],"limitations":["dbt manifest parsing is one-time at definition load; changes to dbt project require Dagster reload","dbt Cloud integration requires API key and network connectivity; no offline mode","Test result tracking requires dbt test execution; no passive test result ingestion from external dbt runs","Macro-generated models may not parse correctly; requires dbt manifest with full parsing"],"requires":["dbt project with manifest.json (dbt >= 1.0)","dagster-dbt library (pip install dagster-dbt)","dbt CLI installed locally or dbt Cloud API credentials"],"input_types":["dbt manifest.json file","dbt project YAML configuration","dbt Cloud API credentials (optional)"],"output_types":["Dagster asset definitions for each dbt model","Asset dependencies matching dbt ref() relationships","Test result events and asset health status"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_5","uri":"capability://tool.use.integration.graphql.api.for.querying.runs.assets.and.events","name":"graphql api for querying runs, assets, and events","description":"Dagster exposes a comprehensive GraphQL API (dagster-graphql package) for querying execution history, asset metadata, and event logs. The API supports complex queries for run status, asset materialization events, sensor/schedule state, and partition status. Clients can subscribe to real-time event streams, trigger runs programmatically, and retrieve asset lineage. The GraphQL schema is auto-generated from Python type definitions, ensuring consistency between CLI/UI and API. The Dagster UI itself uses this API, making it the canonical interface for external integrations.","intents":["Query run history and execution status programmatically for monitoring and alerting","Retrieve asset lineage and dependency information for impact analysis","Trigger asset materializations or backfills via API from external systems","Stream real-time execution events for custom dashboards or monitoring tools"],"best_for":["Teams building custom monitoring and alerting on top of Dagster","Organizations integrating Dagster with external data platforms or BI tools","DevOps teams automating Dagster operations via API"],"limitations":["GraphQL API requires Dagster webserver running; no embedded API for in-process queries","Event log queries can be slow with large run histories (millions of events); no built-in pagination optimization","Real-time subscriptions require WebSocket support; not available in all network environments","API authentication is basic (token-based); no fine-grained RBAC in open-source version"],"requires":["Dagster webserver running (dagster-webserver)","GraphQL client library (graphql-core, Apollo, etc.)","API token or authentication credentials"],"input_types":["GraphQL queries and mutations (text)","Run filters and asset selection expressions","Partition key specifications for backfill requests"],"output_types":["JSON-structured run and asset metadata","Event log entries with timestamps and context","Subscription streams for real-time events"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_6","uri":"capability://data.processing.analysis.event.based.observability.with.structured.event.logs","name":"event-based observability with structured event logs","description":"Dagster's execution model is built on structured events (DagsterEvent objects) that capture all execution details: asset materializations, step outputs, logs, errors, and custom events. Events are persisted to an event log store (configurable: SQLite, PostgreSQL, etc.) with full context including run ID, step key, and timestamp. The system supports custom event types via DagsterEventType, enabling domain-specific observability. Event logs are queryable via GraphQL and CLI, and can be streamed to external systems (Datadog, New Relic, etc.) via event handlers.","intents":["Track detailed execution history with structured events for debugging and auditing","Query event logs to understand asset materialization patterns and failure modes","Stream execution events to external monitoring systems for centralized observability","Emit custom events from asset code for domain-specific metrics and logging"],"best_for":["Data teams requiring detailed execution auditing and debugging capabilities","Organizations integrating Dagster with centralized monitoring platforms","Teams building custom observability and alerting on execution events"],"limitations":["Event log storage can grow rapidly with high-frequency logging; requires periodic cleanup/archival","Event log queries are sequential scans; no built-in indexing for fast filtering by asset or timestamp","Custom event handlers are synchronous; slow handlers can block execution","Event log migration between storage backends is manual; no built-in migration tools"],"requires":["Event log storage backend (SQLite, PostgreSQL, MySQL)","DagsterInstance configuration with event log storage settings","Custom event handler implementations (optional)"],"input_types":["DagsterEvent objects emitted during execution","Custom event types and metadata","Event handler implementations"],"output_types":["Persisted event log entries in configured storage","Queryable event records via GraphQL/CLI","Event streams to external handlers"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_7","uri":"capability://tool.use.integration.resource.based.dependency.injection.with.context.management","name":"resource-based dependency injection with context management","description":"Dagster's resource system provides a declarative way to inject dependencies (database connections, API clients, credentials) into assets and ops. Resources are defined as classes or functions decorated with @resource, and are bound to assets via the context parameter. The system supports resource initialization/cleanup (setup/teardown), resource composition (resources depending on other resources), and environment-specific configuration. Resources are instantiated once per run and passed to all assets in that run, enabling efficient connection pooling and state sharing.","intents":["Inject database connections, API clients, and credentials into assets without hardcoding","Share expensive resources (database pools, API clients) across multiple assets in a run","Configure different resources for different environments (dev, staging, prod) without code changes","Manage resource lifecycle (initialization, cleanup) automatically"],"best_for":["Teams building production data pipelines requiring environment-specific configuration","Organizations with complex resource dependencies and connection pooling requirements","Teams needing to test assets with mock resources"],"limitations":["Resource initialization happens at run start; no lazy initialization for unused resources","Resource composition can create circular dependencies if not carefully managed","Resource state is not shared across runs; each run gets fresh resource instances","Testing resources requires understanding of Dagster's context and resource mocking patterns"],"requires":["Resource definitions using @resource decorator","Asset definitions with context parameter","Resource configuration in job/asset definitions or Dagster instance"],"input_types":["Resource class/function definitions","Resource configuration dictionaries","Context objects passed to assets"],"output_types":["Initialized resource instances available in asset context","Resource initialization/cleanup events","Resource configuration metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_8","uri":"capability://automation.workflow.workspace.and.code.location.management.with.dynamic.loading","name":"workspace and code location management with dynamic loading","description":"Dagster's workspace system organizes definitions (assets, jobs, schedules, sensors) into code locations that can be loaded dynamically. Code locations are Python modules or packages that export a Definitions object, and can be loaded from local filesystem, Python packages, or remote URLs. The workspace.yaml file specifies which code locations to load, enabling multi-team development where each team maintains their own definitions. The system supports dynamic code location discovery and hot-reloading without restarting the daemon, enabling rapid iteration.","intents":["Organize large pipelines into modular code locations by team or domain","Load asset definitions from multiple Python packages without monolithic codebase","Enable hot-reloading of definitions during development without daemon restart","Support multi-team development with independent code location ownership"],"best_for":["Large organizations with multiple teams managing separate data pipelines","Teams using monorepo structure with multiple Python packages","Development teams requiring rapid iteration and hot-reloading"],"limitations":["Code location discovery requires workspace.yaml configuration; no automatic discovery","Hot-reloading can cause state inconsistencies if definitions change during active runs","Remote code locations require network connectivity; no offline fallback","Code location versioning is implicit; no explicit version management for breaking changes"],"requires":["workspace.yaml configuration file","Python modules exporting Definitions objects","Dagster webserver/daemon with code location loader"],"input_types":["workspace.yaml with code location specifications","Python modules with Definitions exports","Code location metadata (name, description, tags)"],"output_types":["Loaded Definitions objects from code locations","Workspace metadata with code location information","Hot-reload notifications"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__cap_9","uri":"capability://tool.use.integration.pipes.framework.for.subprocess.and.external.process.orchestration","name":"pipes framework for subprocess and external process orchestration","description":"Dagster's Pipes framework enables orchestrating external processes (shell scripts, Spark jobs, dbt runs, Python subprocesses) as first-class assets with full observability. Pipes uses a lightweight protocol to capture outputs and events from external processes, streaming them back to Dagster for logging and event tracking. The framework supports multiple execution contexts (local, Kubernetes, Databricks, Spark) with a unified API. External processes emit structured events via the Pipes protocol, enabling Dagster to track their progress and capture outputs without polling or log parsing.","intents":["Execute external processes (shell scripts, Spark jobs) as Dagster assets with full observability","Capture structured outputs from external processes without log parsing","Orchestrate Databricks jobs, Spark clusters, or other external compute as Dagster assets","Stream execution events from external processes back to Dagster for monitoring"],"best_for":["Teams running Spark, Databricks, or other external compute frameworks","Organizations with legacy shell scripts or external tools requiring orchestration","Teams needing to integrate external systems into Dagster pipelines"],"limitations":["Pipes protocol requires external process integration; not transparent for arbitrary executables","External process failures may not propagate cleanly; requires custom error handling","Pipes adds network overhead for event streaming; not suitable for high-frequency event emission","Limited to processes that can emit structured output; incompatible with binary-only tools"],"requires":["dagster-pipes library (pip install dagster-pipes)","External process integration with Pipes protocol (custom code or library support)","Network connectivity between external process and Dagster instance"],"input_types":["External process specifications (command, environment, working directory)","Pipes protocol messages from external process","Asset context and configuration"],"output_types":["Structured events from external process","Process exit codes and output streams","Asset materialization records"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dagster__headline","uri":"capability://data.processing.analysis.data.orchestration.platform.for.ml.and.analytics","name":"data orchestration platform for ml and analytics","description":"Dagster is a modern data orchestration platform designed for building, testing, and managing data pipelines, offering features like software-defined assets and built-in observability, making it a compelling alternative to Airflow.","intents":["best data orchestration platform","data pipelines for machine learning","data orchestration for analytics","Dagster vs Airflow","cloud deployment for data pipelines","data pipeline management tools"],"best_for":["ML workflows","analytics projects"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Dagster core library (pip install dagster)","Understanding of Python decorators and function signatures","Python 3.8+ with type hints support","Custom I/O manager implementation for non-standard storage backends","Understanding of Dagster's resource and context system","Asset daemon running (dagster-daemon process)","Freshness policy definitions on assets","Custom health check implementations (optional)","Asset definitions with partition specifications"],"failure_modes":["Asset definitions are Python-only; no YAML-based asset configuration without custom loaders","Dynamic asset creation at runtime requires AssetSelection patterns; not as flexible as pure task-based DAGs for highly variable workloads","Asset partitioning adds complexity; requires understanding of partition keys and dimension hierarchies","Custom I/O managers require implementing DagsterTypeLoaderContext interface; boilerplate for simple cases","Type checking is runtime-based, not compile-time; Python's duck typing limits static guarantees","Large object serialization can be slow; no built-in compression or streaming for multi-GB assets","I/O manager selection is per-asset; no automatic routing based on asset type or size","Freshness policies are evaluated by asset daemon; no real-time freshness tracking","Custom health checks are synchronous; slow checks can block asset daemon","Health check failures don't automatically trigger remediation; requires separate automation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dagster","compare_url":"https://unfragile.ai/compare?artifact=dagster"}},"signature":"UqD5ST+2xIs0nQ8OwFyn2Kz1AxZFX73L93wd1xIKLwwDIQ4yOhRbtOpLyVXIEZmFORjcYOIqgB1uxUz4xEcWAQ==","signedAt":"2026-06-21T09:04:24.280Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dagster","artifact":"https://unfragile.ai/dagster","verify":"https://unfragile.ai/api/v1/verify?slug=dagster","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}