lakehouse_core
The lakehouse as the signed-in user: Lakekeeper catalog browsing, dataset
files and previews. One implementation behind three front ends - the
JupyterLab Lakehouse explorer, the agents' MCP server and (V1) the VS Code
extension. Lakekeeper decides every permission. Plan:
docs/data-platform/vscode-extension-plan.md.
| Module | What |
|---|---|
config |
catalog URI, warehouse, files root (env or configure()) |
auth |
the user's token: set_provider(fn), LAKEHOUSE_ACCESS_TOKEN, JupyterHub auth_state, or the laptop sign-in (MSAL) |
catalog |
Browser (REST: children, table details); iceberg_catalog, describe_table, preview_table (PyIceberg) |
lakefiles |
dataset files (import lakefiles in notebooks is a shim for this module) |
preview |
first rows / text of csv, tsv, parquet, xlsx, json, jsonl (+ .gz) |
mcp_server |
python -m lakehouse_core.mcp_server - read-only tools for agents (honours agent_mode) |
guide |
the agent guide: guide.md -> variants pod (= the mounted ai-agent/AGENTS.md) and local |
notebooks, notebook_mcp |
python -m lakehouse_core.notebook_mcp - read, edit and run notebooks in the user's JupyterHub server through its collaboration room and the notebook's own kernel, and run a local .py file's # %% cells on a JupyterHub kernel (run_py_cell) (notebooks extra) |
agents |
python -m lakehouse_core agent-setup - guide + MCP config for Claude Code, Cursor, Copilot, Codex |
hub |
the user's JupyterHub server from a laptop: token exchange, start/stop, connection info for remote kernels |
spark |
Spark on the platform from a local Python (V4): from lakehouse_core.spark import spark, same helpers as the Spark kernel; via the Spark Connect gateway |
metadata |
OpenMetadata context (V3a, dormant until a profile sets openmetadata_url) |
rpc |
JSON-lines backend of the VS Code extension (ide/vscode-lakehouse/) |
profiles.json |
one entry per platform: catalog URL, Entra tenant / client / scope, agent_mode |
Deployed by mounting the .py files into the notebook image's
site-packages (jupyterhub.tf in gcp-platform and lakehouse), so a
change needs only a server restart.
On a laptop:
pip install 'drakun-lakehouse-core[all]' # PyPI (import name: lakehouse_core), or the .vsix wheel
python -m lakehouse_core login --profile gcp-dev # browser sign-in once (--device for SSH)
python -m lakehouse_core agent-setup # give your coding agent the guide + tools (lakehouse + jupyterhub MCP)
from lakehouse_core import catalog, lakefiles as lf
df = catalog.iceberg_catalog().load_table("demo.sales").scan(limit=10_000).to_pandas()
lf.ls("files.media.landing/meta_ads")
from lakehouse_core.spark import spark # Spark in the cluster, as you (python -m lakehouse_core spark-check)
spark.table("demo.sales").groupBy("region").count().show()
Changing the guide: edit guide.md, then
python -m lakehouse_core guide --variant pod > ../../../gcp-platform/files/ai-agent/AGENTS.md
(a test fails while they differ).
Tests (stub Lakekeeper, no cluster):
pip install -e '.[test]' && pytest # in this directory
1 maintainer
Metadata
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Release files for drakun-lakehouse-core 0.6.1
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