dtex 0.1.5

dtex (data extraction tool) — an open-source Python EL tool: pipelines as configs, connectors as folders, CLI-first.

dtex

dtex ("data extraction tool") is an open-source, self-hosted Python extract-load (EL) tool. It moves data from a source (an API, a database, a file drop) into a destination (a warehouse, a database, an object store) — and nothing more. Transformation is dbt's job.

The pitch in one line: a CLI-first, dbt-shaped extract-load tool — pipelines are configs, connectors are folders, no UI blackbox. The #1 principle is to keep it as simple as possible.

Install

pip install dtex                          # every baked connector, ready
pip install 'dtex[gcs,s3]'                # add gs:// / s3:// filesystem reads
pip install 'dtex[gcp-secrets]'           # add the GCP Secret Manager resolver
pip install 'dtex[aws-secrets]'           # add the AWS Secrets Manager resolver
pip install 'dtex[vault]'                 # add the HashiCorp Vault resolver

pip install dtex ships every baked source and destination — DuckDB, BigQuery, the filesystem source's local + Parquet path, the REST / Postgres / ShipHero / Stripe sources, the engine, the CLI. Extras stay opt-in for the cloud-storage paths of the filesystem source (gs:// / s3://) and for secret managers (only relevant if your profiles.yml uses secret:// URLs).

dtex requires Python 3.11+. It installs both a CLI (dtex) and an importable library (import dtex).

Usage

dtex init my_project                      # scaffold a project
cd my_project
dtex new source my_api                    # scaffold a source connector
dtex new config my_pipeline               # scaffold a pipeline config
dtex validate                             # check everything
dtex run -p my_pipeline                   # run the pipeline
dtex runs list -p my_pipeline             # show recent run history

A pipeline is one config file binding a source + a destination + a target + params. Run it with dtex run -p <config>. The library equivalent is dtex.run(config="my_pipeline") and returns a structured RunResult.

Pre-baked connectors

Sources: filesystem (CSV/JSONL/Parquet from local, GCS, or S3), rest (paginated REST APIs — 4 pagination strategies, 4 auth modes), postgres (keyset pagination, no OFFSET), shiphero (GraphQL), stripe (resource-as-stream over the REST API).

Destinations: duckdb (zero-config dev default, all 5 capabilities) and bigquery (production warehouse — Parquet-staged via GCS + LOAD jobs, MERGE upserts, cursor-based partitioning).

Engine: per-stream commit + atomic transactions (rollback on failure), state in the destination's _dtex_state table, run records in _dtex_runs, structured JSON-lines logs per run, secret redaction, schema evolution (evolve default, strict opt-in), pipeline-level parallelism with per-destination caps.

Secret managers: GCP Secret Manager, AWS Secrets Manager, HashiCorp Vault — each as an opt-in extra.

Documentation

The full design handbook lives in docs/. Start with 00 — Vision & Naming, 02 — Architecture, 06 — Project Anatomy, 12 — Configs, and 10 — Roadmap and Scope.

Security · Contributing · Code of Conduct

• Security policy — how to report a vulnerability.
• Contributing — dev setup, PR process, how to add a connector.
• Code of Conduct.
• Changelog.

License

Apache License 2.0 — see LICENSE.