An opinionated project layout and toolchain for dlt pipelines — start structured, stay structured.
https://earlybirdvc.github.io/dlt-ops/ ↗// readme
A ready-made structure, toolchain, and set of guides for running many dlt sources in production.
Adopt a worked-out layout, scheduling contract, validation and observability story instead of designing one per project. dlt-ops is a wrapper around dlt, the way dbt wrapped SQL: the primitive stays in charge of the core job (moving data), the wrapper decides how a project is laid out, validated, gated, scheduled, and operated day to day.
It is a convenience toolchain for the common case — scheduled batch ingestion into a warehouse, lake, or local engine at moderate volume. It adds guardrails and ergonomics, not throughput: nothing here makes dlt faster, and high-load pipelines with hard SLAs are better served by purpose-built infrastructure around plain dlt.
What this is — and is not
- A toolchain for dlt specifically. Not a generic ingestion framework: it ships zero connectors and owns no part of the ingest write path — your
@dlt.sourcecode and dlt do all the ingesting. It writes your rows itself in exactly one place: rows an assertion rejects are diverted out of the load into a_dlt_rejectedtable. Drift alerts also carry up to five sample…