digitaltwin-dataspace 0.0.1

DigitalTwin - Dataspace is a Python package that provides a simple and efficient way to create, manage, and query data spaces.

DigitalTwin - Data Space

DigitalTwin Data Sapce is a Python package for creating, managing, and querying data spaces, with a focus on modular data pipelines and digital twin applications. It provides a flexible framework to define, schedule, and run data collectors, harvesters, and handlers, supporting complex data workflows and dependencies.

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Features

• Modular Components: Define custom Collectors, Harvesters, and Handlers for your data workflows.
• Configuration-Driven: Easily configure your data pipeline using TOML files.
• Dependency Management: Automatically resolves and schedules component dependencies.
• CLI Interface: Run, schedule, and manage your data pipeline from the command line.
• Extensible: Add new data sources, processing steps, or outputs by implementing new components.

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Installation

pip install digitaltwin_dataspace

Or, for development:

git clone https://github.com/GaspardMerten/digitaltwin_dataspace.git
cd digitaltwin_dataspace
pip install -e .

Dependencies:

• Python 3.8+
• requests
• SQLAlchemy
• azure-storage-blob
• schedule
• dotenv

(See pyproject.toml for the full list.)

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Usage

Command Line Interface

The main entry point is the dt-dataspace CLI:

dt-dataspace --config-folder path/to/config [options]

Key options:

• --config-folder: Path to the configuration folder (default: config)
• --init-dependencies: Run all harvesters in dependency order
• --handlers: List of handler names to run
• --collectors: List of collector names to run
• --harvesters: List of harvester names to run
• --now: Run harvesters or collectors once and exit
• --port: Port for the handlers server (default: 8888)
• --host: Host for the handlers server (default: localhost)
• --allowed-hosts: Allowed hosts for the handlers server
• --log-level: Set logging level (DEBUG, INFO, etc.)
• --parquetize: List of harvester names to run for parquet output

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Project Structure

digitaltwin_dataspace/
│
├── components/
│   ├── collector.py   # Base Collector class
│   ├── handler.py     # Base Handler class
│   └── harvester.py   # Base Harvester class
│
├── configuration/
│   ├── load.py        # Loads and parses component configuration
│   └── model.py       # Configuration data models
│
├── data/
│   ├── sync_db.py     # Database sync logic
│   ├── retrieve.py    # Data retrieval utilities
│   └── ...            # Other data management modules
│
├── cli.py             # Command-line interface
└── ...

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Components

• Collector:
  Gathers data from external sources. Implement the Collector abstract class and its run() method.

• Harvester:
  Processes or transforms collected data. Implement the Harvester abstract class and its run() method.

• Handler:
  Serves or exposes processed data, e.g., via an API. Implement the Handler abstract class and its run() method.

You can add your own components by subclassing these base classes and registering them in your configuration.

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Configuration

Configuration is done via TOML files in your config folder (default: config/).
Each file can define multiple collectors, harvesters, and handlers, specifying:

• DATA_TYPE, DATA_FORMAT
• PATH (Python import path to your component)
• SCHEDULE (optional, for scheduling)
• SOURCE, DEPENDENCIES (for workflow chaining)
• Other custom parameters

See digitaltwin_dataspace/configuration/load.py for all supported options.

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Author

Gaspard Merten
[e-mail]

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License

Attribution-NonCommercial-ShareAlike 4.0 International

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Links

• Homepage
• Bug Tracker

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