Data & AI Pipelines
Generated. Tested. Governed.
We generate your pipelines — tested, documented, on Iceberg you own. From Glue, Dataflow, notebooks or SQL. No dbt experience required.
A live data & AI pipeline · generated, orchestrated, governed
Ingestion to serving · conceptual
Reference pipeline stack — from ingestion to serving
One path, at your pace
Data pipelines, generated
Your raw Iceberg in. A tested medallion out, on the same open storage.
- •Reads your source schema — keys, relationships, data-quality issues such as mismatched identifiers — and proposes the staging → intermediate → marts plan
- •Writes the dbt models, runs them on your engine, and repairs failures itself until the build is green
- •Row-count reconciliation, range and set tests, null checks written with the pipeline
An AI-ready data platform
The same evidence that satisfies auditors is what makes data safe to hand to AI.
- •Column-level lineage — AI answers can cite where a number came from
- •Personal-data map — AI knows which columns it may show, mask or must avoid
- •Quality tests per build — AI does not learn from broken data
AI pipelines, governed
Text-to-SQL, assistants and agents over the same governed data — with the same rules applied.
- •AI answers gated by the column-level classification your pipelines produced
- •Models you choose, running locally or in your region — nothing leaves your VPC
- •Human approval where you want it; permissive by default, restrictions when you decide
How a pipeline gets built
Also for migrations and re-platforming
| You have | You get |
|---|---|
| Warehouse tables and views | Iceberg tables your other engines can read — reversible, no copy |
| Snowflake-only marts | Iceberg marts served by Snowflake for finance and ClickHouse for real-time |
| Legacy ETL (Informatica, hand-written SQL) | Generated dbt with parity checks against the old output before cut-over |
| Cloud-to-cloud moves | Land on Iceberg once; point Athena, Databricks or Snowflake at it |
Start with one domain
Pick a domain. We generate it on your engine and show you it matches.
Reference implementation
The realtime ingest path (Kafka → ClickHouse) and the lake path (Kafka → Kafka Connect → S3 Iceberg) are runnable today as a 4-container demo bundle. See the open-source backend repo for the docker-compose, SQL, sink connector config, and full runbook.
Repo links open in a new tab. The runbook documents lifecycle, troubleshooting, operational risk register, and the decision log behind every component choice.