FrankBuild governed data pipelines without rebuilding the data platform.

Low-code EL/T for lakehouse teams — source discovery, durable syncs, AI-assisted transforms, pipeline DAGs, ontology publication, and a CLI/SDK surface that works for humans, CI, and agent runtimes.

Quickstart

Concepts

Architecture

Sources land cleanly in Bronze

Register databases, SaaS systems, APIs, files, streams, or warehouses through source patterns. Frank discovers streams, configures sync modes, wraps records in a data envelope, and writes tenant-isolated Iceberg tables.

Transforms are independent data products

Build SQL, dbt, and Python-runner transforms that consume one or many Bronze or transform outputs. Source syncs and transform lifecycles are separate, so EL can fail without invalidating downstream logic.

Pipelines compose real DAGs

Chain transforms into versioned pipeline DAGs with topological validation, immutable snapshots, sandbox runs, activation, pause/resume, and terminal step classification.

AI helps where humans lose time

Martha-backed workflows suggest target schemas, field mappings, pattern parameters, SQL reviews, bespoke transform code, CI fixes, and full pipeline compositions.

Ontology publication is first-class

Map curated Iceberg tables to ontology entity types, sync rows into ontology-core-v2, track sync history, and expose backing datasets for semantic applications.

Pattern catalog, not hardcoded connectors

Source patterns and transform patterns live as declarative config. Add a source, transform, geospatial operation, validation rule, or Python runner without changing the UI.

API, UI, CLI, and SDK stay aligned

The SvelteKit UI, FastAPI routes, frankctl, and frank-sdk all speak the same primitives: sources, streams, transforms, artifacts, runs, pipelines, datasets, schedules, and patterns.

Operable by default

Temporal handles source and transform workflows; Dagster executes materializations; Loki stores logs; OpenTelemetry traces API and worker paths; run records keep the UI fast.