Your stack has already been through one rebuild. This is the next one.
Warehouses gave way to lakehouses; ELT replaced brittle ETL. Each shift served data faster to people. This one is different: agents ask thousands of ad hoc questions, not a fixed report.
Then
Warehouses and lakes, built for BI
Pipelines and semantic layers were designed for fixed reports and human dashboards — not for agents that ask thousands of ad hoc questions.
Recently
Lakehouses, ingest-first ELT
dbt, Spark, and open tables made transforms code-first and storage cheaper — but the stack stayed passive: humans still author, monitor, and heal every job.
Now
The control plane, built for agents
Governed Agent Views, self-healing pipelines, and MCP reachability — your infrastructure stays; it stops being passive.
Your infrastructure stays. It stops being passive.
Ingest-first ELT
Land raw data before transforming it, so nothing is lost to an assumption made too early.
dbt or Spark transforms
Business logic, joins, and validation generated and documented against your declared intent — on the engines you already run.
Land in the control plane
Governed Agent Views, ready for any copilot to query over MCP — not another dashboard.
Run and heal
The agentic runtime monitors and repairs pipelines on exception — humans stay in the loop when it matters, not for every drift.
Modernization, answered.
Does Dagen replace Snowflake, Databricks, dbt, or Spark?+
No. Dagen makes them agent-ready. You keep your warehouse, lakehouse, and transform engines; Dagen adds intent-driven authoring, self-healing, and a control plane copilots can reach over MCP.
Can we retrofit existing jobs instead of starting over?+
Yes. Modernize the logic, not the lock-in. You retrofit existing jobs piece by piece into pipelines that are self-healing and machine-readable — without rip-and-replace.
Do our dbt models and Spark jobs stay?+
Yes. Transforms still run on dbt or Spark. Dagen generates and documents logic against your declared intent, then finishes at the control plane so agents can use the result.
How is this different from a BI semantic layer?+
BI semantic layers serve fixed reports and human dashboards. The control plane serves agents: governed Agent Views, continuous lineage, and MCP reachability for ad hoc questions — humans on exception only.
What happens when schemas drift?+
Legacy ETL and most modern stacks still require manual intervention. With Dagen, drift is detected and repaired automatically; engineers review when the runtime surfaces an exception.
Bring your stack. We'll add the intelligence layer.
Book a technical demo scoped to the pipelines you already run — ingest, transform, and finish at the control plane, without locking into a new platform.