hyperion-sdk 1.0.2

A headless ETL / ELT / data pipeline and integration SDK for Python.

Hyperion

A headless ETL / ELT / data pipeline and integration SDK for Python.

📚 Documentation: https://tomasvotava.github.io/hyperion/

Hyperion organises data assets (a data catalog), validates them against Avro schemas, abstracts storage/queue/cache/secrets backends behind ports and adapters, and gives you a framework for writing data sources. The same code runs on a laptop (local filesystem) or on AWS (S3/DynamoDB/SQS) — the wiring is configuration, not code.

Features

• Data Catalog — manage and organise data assets across backends
• Schema management — validate and store Avro schemas per asset
• Ports & adapters — swap S3/DynamoDB/SQS for local filesystem via config
• Source framework — define sources that extract data into the catalog
• Caching — in-memory, local file, and DynamoDB caching
• Asynchronous processing — utilities for async operations and task queues
• CLI runner — run sources standalone or in "Argo Workflow" mode
• Geo utilities — Haversine math in the lite core; Google Maps via [geo]
• Asset collections — a typed, declarative interface over groups of assets

Installation

pip install 'hyperion-sdk[catalog]'
# or
poetry add 'hyperion-sdk[catalog]'

As of 1.0.0 the default install is a slim lite core; heavy backends are opt-in extras — install only the ones you use:

Extra	Enables
hyperion-sdk[aws]	DynamoDB cache/keyval, S3 storage/schema, SQS queue, AWS Secrets Manager
hyperion-sdk[data]	pandera↔polars typing, asset schemas, SpatialKMeans
hyperion-sdk[catalog]	avro-backed Catalog (works with local filesystem storage alone)
hyperion-sdk[geo]	Google Maps geocoding (Haversine math stays lite)
hyperion-sdk[snappy]	snappy-compressed filesystem cache / DynamoDB keyval
hyperion-sdk[all]	everything (parity with the pre-1.0 full install)

We follow Semantic Versioning — pin with a selector such as ^1.0.0. Upgrading from a pre-1.0 release? Import paths moved and the Catalog constructor changed — see the migration guide (also pinned at the top of CHANGELOG.md).

Quickstart

from hyperion.catalog.catalog import Catalog
from hyperion.domain.assets import DataLakeAsset
from datetime import datetime, timezone

catalog = Catalog.from_config()
asset = DataLakeAsset(name="customer_data", date=datetime.now(timezone.utc), schema_version=1)
catalog.store_asset(asset, [{"id": 1, "name": "Customer 1"}])

A runnable, no-AWS walkthrough is in the first DataLakeAsset tutorial.

Documentation

Full documentation is published at https://tomasvotava.github.io/hyperion/ and follows the Diataxis structure:

• Tutorials — learning-oriented walkthroughs
• How-to guides — task-oriented recipes
• Reference — auto-generated API reference + migration guide
• Explanation — architecture and design

Development

Hyperion uses Poetry:

git clone https://github.com/tomasvotava/hyperion.git
cd hyperion
poetry install
poetry run pre-commit install

poetry run pytest                  # run tests
poetry run pre-commit run -a       # ruff + mypy + hooks
poetry install --with docs         # docs toolchain
poetry run mkdocs serve            # preview the docs site locally

The project uses ruff (linting), mypy (type checking), and commitizen (conventional commits). See the architecture explanation for the layered design.

Contributing

See CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License — see the LICENSE file for details.