{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"feast","slug":"feast","name":"Feast","type":"repo","url":"https://github.com/feast-dev/feast","page_url":"https://unfragile.ai/feast","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"feast__cap_0","uri":"capability://data.processing.analysis.point.in.time.correct.historical.feature.retrieval.for.training.datasets","name":"point-in-time correct historical feature retrieval for training datasets","description":"Generates training datasets by performing temporal joins between entity timestamps and feature values, ensuring that only historical feature data available at each training example's timestamp is included. Uses a registry-backed lookup system to resolve feature definitions and executes offline store queries with time-windowed predicates, preventing training-serving skew by guaranteeing models train on the exact feature values that would have been available during inference at that point in time.","intents":["Generate training datasets that match the historical state of features at specific timestamps","Prevent training-serving skew by ensuring training data reflects features available at inference time","Build reproducible training pipelines that can be re-run with consistent historical snapshots"],"best_for":["ML teams building production models where training-serving consistency is critical","Data scientists working with time-series or event-driven features requiring temporal accuracy"],"limitations":["Requires offline store to maintain full historical feature data; pruning old data breaks reproducibility","Performance degrades with very large entity sets (millions+) due to join cardinality","Point-in-time joins assume monotonically increasing timestamps; out-of-order events may produce incorrect results"],"requires":["Python 3.9+","Configured offline store (Parquet, BigQuery, Snowflake, Spark, etc.)","Entity keys and event timestamps in source data","Feature definitions registered in Feast registry"],"input_types":["entity_df (DataFrame with entity keys and event timestamps)","feature_refs (list of feature names to retrieve)"],"output_types":["pandas.DataFrame or Polars DataFrame with entity keys, timestamps, and historical feature values"],"categories":["data-processing-analysis","ml-feature-engineering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_1","uri":"capability://automation.workflow.feature.materialization.from.batch.sources.to.online.stores","name":"feature materialization from batch sources to online stores","description":"Orchestrates scheduled or on-demand jobs that read feature values from offline data sources (data warehouses, data lakes, batch pipelines) and writes them to low-latency online stores (Redis, DynamoDB, PostgreSQL, SQLite) for real-time serving. Uses a Provider abstraction that delegates to compute engines (Spark, Kubernetes, local) and coordinates with the registry to determine which features to materialize, their freshness requirements, and target online store schemas.","intents":["Keep online feature stores fresh by periodically syncing computed features from batch pipelines","Enable sub-100ms feature lookups for real-time inference by pre-materializing features","Manage feature freshness SLAs with configurable materialization schedules and incremental updates"],"best_for":["ML teams operating real-time inference systems requiring <100ms feature latency","Organizations with batch feature computation pipelines (Spark, dbt, SQL) needing online serving"],"limitations":["Materialization jobs are pull-based; no native support for push-based streaming without custom integrations","Online store write throughput becomes bottleneck at scale (>10k features, >1M entities); requires careful partitioning","No built-in deduplication or idempotency guarantees; duplicate writes can occur if jobs retry"],"requires":["Python 3.9+","Configured offline store with readable data sources","Configured online store with write access","Compute engine (Spark, Kubernetes, or local Python)","Feature definitions with materialization config (schedule, TTL)"],"input_types":["FeatureView definitions with batch_source and online_store config","Materialization request (start_date, end_date, feature_refs)"],"output_types":["Materialized features written to online store; returns job status and row counts"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_10","uri":"capability://tool.use.integration.web.ui.for.feature.discovery.and.monitoring","name":"web ui for feature discovery and monitoring","description":"Provides a web-based interface for browsing feature definitions, viewing feature statistics, and monitoring materialization jobs. Built with React frontend and Python Flask backend, it queries the registry to display feature schemas, data sources, and lineage. Integrates with feature store to show materialization status and feature freshness metrics.","intents":["Discover available features and their schemas without CLI or code","Monitor materialization job status and feature freshness","Understand feature lineage and dependencies"],"best_for":["Non-technical stakeholders (product managers, analysts) exploring available features","ML teams monitoring feature store health and materialization status"],"limitations":["UI is read-only; no ability to apply feature definitions or trigger materialization from UI","Requires separate deployment; adds operational overhead","Performance degrades with large feature catalogs (1000+ features)"],"requires":["Python 3.9+","Feast feature store configured and running","Network access to UI server (port 8501 by default)","Registry with feature definitions"],"input_types":["Feature store configuration and registry"],"output_types":["Web UI displaying feature catalog, statistics, and monitoring dashboards"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_11","uri":"capability://automation.workflow.provider.based.compute.engine.abstraction.for.materialization","name":"provider-based compute engine abstraction for materialization","description":"Abstracts compute engines (Spark, Kubernetes, local Python) behind a unified Provider interface that handles job submission, monitoring, and result retrieval. Providers are responsible for executing materialization jobs, reading from offline stores, and writing to online stores. Supports custom providers for integration with proprietary compute systems (Airflow, Prefect, Dagster).","intents":["Execute materialization jobs on different compute engines without changing feature definitions","Integrate Feast with existing orchestration systems (Airflow, Kubernetes, Spark)","Scale materialization to handle large feature sets by leveraging distributed compute"],"best_for":["ML teams with existing compute infrastructure (Spark clusters, Kubernetes) wanting Feast integration","Organizations needing to scale materialization beyond single-machine capacity"],"limitations":["Custom providers require significant development effort; no template or framework provided","Provider interface is low-level; teams must handle job monitoring, error handling, and retries","No built-in cost optimization; expensive compute engines (Spark, Kubernetes) may be overkill for small feature sets"],"requires":["Python 3.9+","Configured compute engine (Spark, Kubernetes, or local Python)","Provider implementation (built-in or custom)","Access to offline and online stores from compute engine"],"input_types":["Materialization job definition (feature_refs, date range, online_store)"],"output_types":["Job status and monitoring information","Materialized features written to online store"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_12","uri":"capability://data.processing.analysis.entity.and.feature.schema.management.with.type.system","name":"entity and feature schema management with type system","description":"Defines a type system for entities and features that maps Python types to data warehouse types (int, float, string, timestamp, array, struct). Automatically infers schemas from data sources and validates feature values at materialization and serving time. Supports complex types (arrays, structs) for data warehouses that support them (BigQuery, Snowflake) and serializes them for online stores that don't.","intents":["Define entity and feature schemas declaratively without manual type mapping","Validate feature values at materialization and serving to catch data quality issues","Support complex types (arrays, structs) across heterogeneous stores"],"best_for":["ML teams with complex feature schemas (nested objects, arrays) requiring type safety","Organizations needing data quality validation at feature store boundaries"],"limitations":["Type validation is basic; no advanced constraints (min/max values, regex patterns)","Complex type serialization adds overhead; arrays and structs are slower than scalar types","Schema evolution is manual; changing feature types requires explicit migration"],"requires":["Python 3.9+","Entity and Feature definitions with type annotations","Data sources with compatible schemas"],"input_types":["Python type annotations (int, float, str, datetime, List, Struct)"],"output_types":["Inferred schemas for data sources","Validated feature values at materialization and serving"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_2","uri":"capability://tool.use.integration.multi.store.feature.serving.via.http.grpc.apis","name":"multi-store feature serving via http/grpc apis","description":"Exposes a feature server (Python, Go, or Java implementation) that accepts entity keys and returns feature values by querying online stores in real-time. The server maintains an in-memory cache of feature definitions from the registry, performs feature lookups with configurable fallback logic (online-to-offline), and supports batch requests for efficiency. Uses protobuf-based request/response schemas for language-agnostic serialization and supports both HTTP REST and gRPC transports.","intents":["Serve features to ML models in production with sub-100ms latency","Support batch feature requests for inference on multiple entities simultaneously","Implement fallback logic to fetch from offline stores if online store misses"],"best_for":["ML teams deploying real-time inference services requiring low-latency feature access","Organizations needing language-agnostic feature serving (Python models, Java services, Go microservices)"],"limitations":["Feature server is stateless; no built-in request deduplication or caching across requests","Batch request size is limited by online store query performance; typical max 1000-10k entities per request","Fallback to offline store adds latency (100ms-1s); not suitable for strict <50ms SLAs"],"requires":["Python 3.9+ (for Python server) or Go 1.18+ (for Go server) or Java 11+ (for Java server)","Configured online store with read access","Configured offline store (required for fallback logic)","Feature definitions registered in Feast registry","Network access to feature server (HTTP port 6566 or gRPC port 6567 by default)"],"input_types":["GetOnlineFeaturesRequest (entity_rows with entity keys, feature_refs)","GetOnlineFeaturesRequestV2 (entity_df with DataFrame of entities)"],"output_types":["GetOnlineFeaturesResponse (feature_vectors with values, statuses, event_timestamps)","JSON or protobuf serialized responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_3","uri":"capability://memory.knowledge.feature.definition.versioning.and.registry.based.discovery","name":"feature definition versioning and registry-based discovery","description":"Maintains a centralized registry (backed by local SQLite, PostgreSQL, or cloud storage) that stores feature definitions, data sources, and metadata as versioned objects. Features are defined as Python classes (FeatureView, StreamFeatureView) with declarative schemas, transformations, and freshness requirements. The registry enables discovery via CLI and SDK, tracks feature lineage, and ensures consistency across training and serving by providing a single source of truth for feature semantics.","intents":["Define features once and reuse them across training pipelines and serving systems","Discover available features and their schemas without manual documentation","Track feature lineage and dependencies to understand impact of data source changes"],"best_for":["ML teams with multiple models sharing features, requiring centralized feature governance","Organizations building feature catalogs for cross-team feature reuse"],"limitations":["Registry is not a real-time feature catalog; changes require explicit apply/deploy steps","No built-in access control; registry access is all-or-nothing (no column-level or feature-level RBAC)","Versioning is implicit (based on definition hash); no explicit semantic versioning or deprecation warnings"],"requires":["Python 3.9+","Registry backend (SQLite, PostgreSQL, GCS, S3, or local file system)","Feature definitions in Python (feature_store.yaml or Python modules)","feast CLI or Python SDK to apply definitions"],"input_types":["Python class definitions (FeatureView, StreamFeatureView, Entity)","YAML configuration (feature_store.yaml)"],"output_types":["Registry metadata (protobuf-serialized FeatureView, Entity, DataSource objects)","Feature catalog (list of available features with schemas and descriptions)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_4","uri":"capability://automation.workflow.streaming.feature.ingestion.via.push.api","name":"streaming feature ingestion via push api","description":"Accepts real-time feature updates via HTTP/gRPC push API that writes directly to online stores without requiring batch materialization. Supports both individual feature updates and batch pushes, with configurable schemas and validation. Uses StreamFeatureView definitions to declare streaming features and integrates with Kafka, Kinesis, or custom event sources via connector patterns.","intents":["Ingest real-time features (user behavior, fraud signals, dynamic pricing) without batch latency","Update online stores with streaming data from event sources (Kafka, Kinesis, webhooks)","Support low-latency feature updates for time-sensitive use cases (fraud detection, recommendations)"],"best_for":["ML teams building real-time systems requiring sub-second feature freshness","Organizations with event-driven architectures (Kafka, Kinesis) needing feature store integration"],"limitations":["Push API is write-only; no built-in deduplication or exactly-once semantics (at-least-once delivery)","No native ordering guarantees; out-of-order events may overwrite newer values with stale data","Requires custom connectors for event sources; no built-in Kafka/Kinesis consumers"],"requires":["Python 3.9+","Configured online store with write access","StreamFeatureView definitions with schema","Network access to feature server push API (HTTP or gRPC)","Custom connector code or event source integration"],"input_types":["PushFeaturesRequest (entity_rows with entity keys and feature values)","Streaming events from Kafka, Kinesis, or HTTP webhooks"],"output_types":["PushFeaturesResponse (status, row counts)","Features written to online store"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_5","uri":"capability://data.processing.analysis.transformation.based.feature.computation.with.sql.and.python","name":"transformation-based feature computation with sql and python","description":"Supports on-demand feature computation via SQL transformations (for data warehouse-native features) and Python transformations (for custom logic). Transformations are defined declaratively in FeatureView definitions and executed at training time (for offline features) or materialization time (for online features). Uses a transformation engine that compiles Python code to SQL when possible (for Spark/BigQuery) or executes Python UDFs for complex logic.","intents":["Define computed features (aggregations, ML model outputs, business logic) without separate pipelines","Reuse transformation logic across training and serving by defining it once in Feast","Execute feature transformations in the data warehouse for efficiency (push-down computation)"],"best_for":["ML teams with feature engineering logic in SQL or Python wanting to centralize it","Organizations using data warehouses (BigQuery, Snowflake, Spark) for feature computation"],"limitations":["SQL transformations are limited to data warehouse-supported syntax; complex Python logic requires UDFs","Python transformations execute client-side during training, adding latency; not suitable for large-scale feature generation","No automatic optimization; inefficient transformations (nested loops, full table scans) are not rewritten"],"requires":["Python 3.9+","SQL transformation: Configured offline store with SQL support (BigQuery, Snowflake, Spark, DuckDB)","Python transformation: Python 3.9+ with required libraries (pandas, numpy, etc.)","FeatureView definitions with transformation parameter"],"input_types":["SQL string with source table references","Python function with pandas/Polars DataFrame input"],"output_types":["Computed feature values (numeric, string, timestamp, etc.)","Materialized features in online store or training dataset"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_6","uri":"capability://data.processing.analysis.multi.backend.offline.store.abstraction.for.training.data.generation","name":"multi-backend offline store abstraction for training data generation","description":"Abstracts offline data sources (Parquet files, data warehouses, data lakes) behind a unified OfflineStore interface that handles schema inference, query compilation, and result retrieval. Supports BigQuery, Snowflake, Spark, DuckDB, PostgreSQL, and Parquet-based stores, allowing teams to switch backends without changing feature definitions. Uses a DataSource abstraction to declare where features are stored and automatically generates appropriate SQL queries for each backend.","intents":["Generate training datasets from heterogeneous data sources (data warehouse, data lake, local files)","Switch offline stores without rewriting feature definitions or training pipelines","Leverage data warehouse compute for efficient feature retrieval at scale"],"best_for":["ML teams with data in multiple systems (BigQuery, Snowflake, S3 Parquet) needing unified access","Organizations migrating between data warehouses or adding new data sources"],"limitations":["Query performance depends on underlying store; no query optimization or cost control","Schema inference is automatic but may fail for complex types (nested structs, arrays); requires manual schema specification","Cross-store joins are not supported; all features must come from same offline store"],"requires":["Python 3.9+","Configured offline store (BigQuery, Snowflake, Spark, DuckDB, PostgreSQL, or Parquet)","Credentials/access to data sources","DataSource definitions pointing to tables/paths"],"input_types":["DataSource definitions (table name, path, query)","Entity DataFrame with keys and timestamps"],"output_types":["Training dataset (pandas/Polars DataFrame with historical feature values)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_7","uri":"capability://data.processing.analysis.multi.backend.online.store.abstraction.for.real.time.feature.serving","name":"multi-backend online store abstraction for real-time feature serving","description":"Abstracts online feature storage (Redis, DynamoDB, PostgreSQL, SQLite, Cassandra) behind a unified OnlineStore interface that handles schema mapping, serialization, and low-latency lookups. Supports both key-value stores (Redis, DynamoDB) and relational stores (PostgreSQL, SQLite) with automatic schema creation and index management. Uses a consistent key format across stores to enable switching backends without data migration.","intents":["Store materialized features in low-latency online stores for real-time serving","Switch online stores without re-materializing features or rewriting serving code","Support multiple online stores simultaneously (e.g., Redis for hot features, PostgreSQL for cold features)"],"best_for":["ML teams deploying real-time inference requiring <100ms feature latency","Organizations with heterogeneous infrastructure (cloud and on-premises) needing flexible storage"],"limitations":["Online store write throughput becomes bottleneck at scale; requires careful partitioning and batch sizing","No built-in replication or failover; high availability requires external mechanisms (Redis Sentinel, DynamoDB Global Tables)","Schema evolution is manual; adding new features requires explicit schema updates in some stores"],"requires":["Python 3.9+","Configured online store (Redis, DynamoDB, PostgreSQL, SQLite, Cassandra, etc.)","Network access and credentials to online store","Feature definitions with online_store config"],"input_types":["Materialized features (entity keys, feature values, timestamps)","Feature lookup requests (entity keys, feature_refs)"],"output_types":["Feature values retrieved from online store","Materialized features written to online store"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_8","uri":"capability://automation.workflow.feature.store.configuration.and.environment.management","name":"feature store configuration and environment management","description":"Manages Feast configuration via feature_store.yaml files that declare offline stores, online stores, registries, and compute engines. Supports environment-specific overrides (dev, staging, prod) and integrates with Python SDK to load configuration at runtime. Uses a RepoConfig abstraction that validates configuration and initializes store connections, enabling teams to manage infrastructure as code.","intents":["Define feature store infrastructure (stores, registries, compute) in version-controlled YAML","Manage environment-specific configurations (dev with SQLite, prod with BigQuery + Redis)","Initialize feature store connections programmatically from configuration"],"best_for":["ML teams using infrastructure-as-code practices and version control","Organizations managing multiple Feast deployments (dev, staging, prod)"],"limitations":["Configuration is static; no runtime store switching without reloading FeatureStore object","No built-in secret management; credentials must be provided via environment variables or external systems","Validation is basic; complex constraints (e.g., 'online store must match offline store type') are not enforced"],"requires":["Python 3.9+","feature_store.yaml file in Feast project root","Credentials for configured stores (via environment variables or files)"],"input_types":["feature_store.yaml (YAML configuration file)"],"output_types":["RepoConfig object with parsed configuration","Initialized FeatureStore instance with connected stores"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__cap_9","uri":"capability://automation.workflow.feature.store.cli.for.development.and.operations","name":"feature store cli for development and operations","description":"Provides command-line interface for common Feast operations: applying feature definitions to registry, materializing features, retrieving training data, and managing online stores. Commands are implemented as Python functions that interact with FeatureStore and Provider abstractions, enabling both interactive development and scripted automation. Supports YAML-based feature definitions and integrates with Python SDK for programmatic access.","intents":["Apply feature definitions to registry without writing Python code","Materialize features on-demand or schedule materialization jobs","Debug feature definitions and retrieve training data for model development"],"best_for":["Data scientists and ML engineers developing features interactively","DevOps teams automating feature store operations in CI/CD pipelines"],"limitations":["CLI is synchronous; long-running operations (materialization) block until completion","Error messages are generic; debugging requires reading logs or Python SDK","No built-in progress tracking for long-running jobs"],"requires":["Python 3.9+","Feast installed (pip install feast)","feature_store.yaml in current directory or parent directories","Credentials for configured stores"],"input_types":["CLI arguments (feature names, date ranges, entity keys)","YAML feature definitions"],"output_types":["Console output (status, row counts, errors)","Training datasets (Parquet, CSV)","Materialization job status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"feast__headline","uri":"capability://data.processing.analysis.open.source.feature.store.for.machine.learning","name":"open-source feature store for machine learning","description":"Feast is an open-source feature store designed to manage and serve machine learning features across the full ML lifecycle, ensuring point-in-time correctness and feature versioning for both training and online inference.","intents":["best open-source feature store","feature store for machine learning","how to manage ML features","feature store solutions for real-time inference","feature management tools for ML lifecycle"],"best_for":["teams using machine learning","data scientists","ML engineers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Configured offline store (Parquet, BigQuery, Snowflake, Spark, etc.)","Entity keys and event timestamps in source data","Feature definitions registered in Feast registry","Configured offline store with readable data sources","Configured online store with write access","Compute engine (Spark, Kubernetes, or local Python)","Feature definitions with materialization config (schedule, TTL)","Feast feature store configured and running","Network access to UI server (port 8501 by default)"],"failure_modes":["Requires offline store to maintain full historical feature data; pruning old data breaks reproducibility","Performance degrades with very large entity sets (millions+) due to join cardinality","Point-in-time joins assume monotonically increasing timestamps; out-of-order events may produce incorrect results","Materialization jobs are pull-based; no native support for push-based streaming without custom integrations","Online store write throughput becomes bottleneck at scale (>10k features, >1M entities); requires careful partitioning","No built-in deduplication or idempotency guarantees; duplicate writes can occur if jobs retry","UI is read-only; no ability to apply feature definitions or trigger materialization from UI","Requires separate deployment; adds operational overhead","Performance degrades with large feature catalogs (1000+ features)","Custom providers require significant development effort; no template or framework provided","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":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","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=feast","compare_url":"https://unfragile.ai/compare?artifact=feast"}},"signature":"sI5mPZyl4MH5YSMP49jEX88JHVOKEQw0qS8ZnxlYjxy9l+ZqzaANzC1wxoLpd7eijkA9JgLdrT7FBrF/LYzPAQ==","signedAt":"2026-06-20T09:11:50.393Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/feast","artifact":"https://unfragile.ai/feast","verify":"https://unfragile.ai/api/v1/verify?slug=feast","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"}}