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Find available data in Hopsworks from feature groups, data sources, free-text search, and files.
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Basé sur la classification professionnelle SOC
| name | hops-data-discovery |
| description | Find available data in Hopsworks from feature groups, data sources, free-text search, and files. |
Find available data in a Hopsworks project across four surfaces: feature groups (and views / training data), free-text search, data sources, and files. Prefer the hops CLI for data discovery over writing Python.
Discovery enables feature reuse: a model that reuses an existing feature does not need a new feature pipeline, so search the feature registry first before engineering features from scratch. The commands below browse that registry (definitions, schemas, tags, statistics).
hops fg list # all feature groups in the project
hops fg info <name> --version 1 # metadata (online flag, PK, event time)
hops fg features <name> --version 1 # schema with PK / partition flags
hops fg preview <name> --version 1 --n 10 # first rows (note: preview <name>, not <name> preview)
hops fv list # feature views
hops search ls <term> # match FGs, FVs, training datasets, features
hops search ls <term> --type feature_group # restrict by type
hops search ls <term> --global # search across shared/other projects
hops datasource list # configured connectors (Snowflake, S3, JDBC, ...)
hops datasource info <name>
hops files list [path] # browse the project filesystem
Use when writing and deploying an interactive agent (e.g. a LlamaIndex program) as a served Hopsworks deployment.
Use when writing Streamlit or custom Python apps for Hopsworks, deploying from HopsFS or Git, migrating legacy apps off `APP_BASE_URL_PATH`, or managing app routing/readiness, monitoring, and public sharing. Auto-invoke when user wants to create a dashboard, deploy a Python app to Hopsworks, configure app routing/readiness, or access the feature store from an app. Input an app source + env + memory; output a running app and its URL.
Mount or ingest a table from a supported datasource. Mount tables from a datasource as an external feature group or ingest data into a new feature group using DLTHub. Auto-invoke when user works with external data (Snowflake, BigQuery, Redshift, S3, ADLS, GCS, JDBC, SQL, Databricks Unity Catalog, Postgres, MySQL, Oracle, SAP, MongoDB, CRM, REST APIs).
Use when writing Python code that creates, inserts into, or manages tables or feature groups. Auto-invoke when user writes feature pipelines, feature engineering code, or asks about feature group best practices (online vs offline, batching, OOM, materialization, embeddings, statistics).
Use when a Hopsworks job, app, or deployment needs Python libraries that are not in a base environment. Clone a base environment and install requirements or a wheel into the clone. Auto-invoke when the user hits a missing-package error in a job/app/deployment, asks to install custom dependencies, add a pip requirement, install a wheel, or pick which Python environment a workload should run in.
Use when the user asks where something lives in the Hopsworks UI or how to reach a page (Data Sources, Feature Groups, Feature Views, Model Registry, Deployments, Apps, Agents, Jobs, Jupyter, Project Settings). Knowledge skill: the project-scoped sidebar layout and what sits under each section, so you can point users to the right place.