Run end-to-end ETL with dltHub and your existing dbt transformation layer

Run ingestion and dbt transformations in one orchestrated workflow without rebuilding your transformation layer

Claude Code, Cursor or Codex

Your coding Agent

operates through

the agentic layer

dltHub AI harness

Agentic primitives to build, run, and fix pipelines

dltHub context catalog

Lineage, schema, data quality, governance, run state, and API context assets

grounds & runs

dlt Sources

Logs
Traces
OpenCode traces
Token usage
Tool calls
Model latency
Costs

dltHub

the managed infrastructure layer

Ingestion
dbt models
Transformations
Orchestration
Managed infrastructure

Data Apps

Dashboard
Lineage
Context
Metrics

dltHub — agentic data layer + managed infrastructureYour agent & external tools

An AI coding agent operates through dltHub's agentic layer, which grounds and runs a data plane that turns raw telemetry into spend dashboards.

Put this blueprint to work

Talk it through with the dltHub team and we will help you ship it to production.

How it works

dltHub lets teams combine native ingestion with the transformation tools they already use. Its modular, composable architecture allows teams to build on existing investments instead of replacing them. Existing dbt projects can plug directly into dltHub, bringing ingestion and transformations into one operational workflow without rewriting the transformation layer. This blueprint demonstrates that setup end to end, from connecting an existing dbt project to dltHub through deployment and production execution.

The existing dbt project stays unchanged, while dltHub connects it directly to ingestion in one operational workflow. Transformations can be triggered automatically after successful ingestion runs. Shared lineage, schemas, run traces, and quality context give AI agents the information they need to investigate issues end-to-end: from resulting metrics back through dbt to the source pipeline. This reduces manual debugging, removes tool handoffs, and speeds up issue resolution.

If your dbt workload currently runs on another orchestrator, moving it to dltHub can be AI-assisted. Agents analyze lineage, dependencies, migration risks, and output parity, reducing the manual work required to bring an existing production workload onto the platform.

Key features

Keep your existing transformations without changing dbt models, SQL, macros, or dependencies.
Resolve data issues faster with ingestion and transformation connected through shared lineage and execution context.
Run dbt alongside ingestion on dltHub with orchestration and runtime managed in one operational workflow.
Move existing workloads with AI assistance using lineage, dependency, risk, and data-quality checks to support a safe production transition.

How to get it

Use an agent to plug your dbt models into dltHub and integrate it with your ingestion workflow.
Share your current stack with our team. We’ll scope the setup, define the migration approach, and send you a proposal for the rollout.

Pricing

dltHub from $11,900/month: the platform your transformations run on.
Contact dltHub for a 30-minute scoping call on your existing transformation layer.

Put this blueprint to work

Talk it through with the dltHub team and we will help you ship it to production.

Find more blueprints