Data Platform

KNIME orchestration & lakehouse
Step 01 · Ingest

Load your data

Drop files into the lake, wire up any external credentials, and confirm what landed in the warehouse.

Data lake

NameSizeModifiedActions

Files land in the object store and are readable from KNIME via the Generic S3 Connector (endpoint + credentials: see Connection info; path-style access on). Tip: partition by date, e.g. sales/year=2026/month=07/orders.parquet.

Warehouse tables

TableRowsColumnsActions

Quick peek only — for charts and SQL head to the Visualize step. In KNIME, read these via PostgreSQL Connector (host postgres, database warehouse).

Step 02 · Build

Build a workflow

Author the transformation in KNIME, export a .knwf, and register it under this data product.

Upload workflow

Workflows

Workflow Manage

Keep versions and roll back from here. Cadence, on/off and run history live in the Schedule & run step.

Step 03 · Orchestrate

Schedule & run

Set cadences, switch workflows on, trigger runs, and chain them into pipelines.

Workflow schedule

Workflow Schedule Enabled Last run Actions

Pipelines

Pipeline Steps (stages run in order, same stage = parallel) Schedule Enabled Last run Actions
Superset — sign in with your platform account (Keycloak SSO). If the frame stays on a login screen, use “Open in new tab” once, then return. The Warehouse connection is pre-configured; tables live under Datasets → + Dataset. Open in new tab ↗
Admin · Configuration

Data products

Create a data product to provision its Keycloak group, S3 prefixes, warehouse schema and service account in one step.

Name Warehouse schema S3 paths Service user Actions

Creating a data product sets up everything at once: Keycloak group (assign users there), S3 prefixes in raw/curated, warehouse schema, and a service account the executor uses for this product's workflows. See docs/access-management.md for the full guide.