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D.ata C.ollection F.ramework - Define a data source, run dcf, query the data

Project description

dcf -- D.ata C.ollection F.ramework

PyPI Python License

uvx --from dcf-core dcf init

How it works

  1. Define a data collector in YAML for any http data source
  2. Run it with dcf run
  3. Query your new data

Quickstart

Get real data. From an API. Into your Lakehouse. Query it with SQL. In 5 lines.

mkdir dcf-demo && cd dcf-demo
uvx --from dcf-core dcf init
uv sync
uv run dcf run so_questions
uv run dcf query 'SELECT * FROM stackoverflow.so_questions'

dcf init creates pyproject.toml, profiles.yml, .gitignore, collectors/, and an example collector.


Example

dcf collector

name: so_questions
namespace: stackoverflow

source:
  type: http
  url: https://api.stackexchange.com/2.3/questions
  response:
    records_path: items
  params:
    - name: site
      type: string
      value: stackoverflow
    - name: tagged
      type: string
      value: "python;data-engineering"
  schema:
    columns:
      - name: question_id
        path: question_id
        type: integer
      - name: title
        path: title
        type: string
      - name: creation_date
        path: creation_date
        type: integer

cadence:
  strategy: incremental
  primary_key: question_id
  iterate:
    - type: date_range
      params: [fromdate, todate]
      format: "%s"
      start: "2025-01-01"
      end: today
      step: 30 days

dcf run

uv run dcf run so_questions

dcf query

uv run dcf query 'SELECT * FROM stackoverflow.so_questions LIMIT 5'

More features


Contributing

git clone https://github.com/zephschafer/dcf && cd dcf && uv sync

Point a local project at your checkout:

[tool.uv.sources]
dcf-core = { path = "../dcf", editable = true }

To verify changes:

uv run dcf run so_questions
uv run dcf query 'SELECT * FROM stackoverflow.so_questions'

Releasing: bump version in pyproject.toml and push to main — GitHub Actions publishes to PyPI automatically.


Package structure

dcf/
├── cli.py              Entry point (Typer)
├── config/
│   ├── models.py       Pydantic models for collector YAML
│   └── loader.py       YAML loading + env var resolution
├── engine/
│   ├── runner.py       Outer loop (iterate → fetch → project → write)
│   ├── fetcher.py      HTTP and Python source fetchers
│   ├── iterator.py     Date range and categorical iteration
│   ├── projector.py    Schema projection and path extraction
│   └── transforms.py   Column transforms
├── writer/
│   └── iceberg.py      Write strategies (incremental / append / full_refresh)
└── gcp/                GCP auth, provisioning, Terraform wrappers

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