Extented Synthetic Data Generator ===================================== .. NOTE:: Understand the purpose of each component from the :ref:`architecture`. SDG uses `pluggy `_ to develop plug-in systems, which is based on the `entry-points of Python project `_. A plugin project is made up of three parts: - A class, inherits from the ``register_type`` of :ref:`Manager `, which contains your own logic. - A register function, which's name is defined(decorated) by ``@hookspec``. and you need to implement it and use ``@hookimp`` to declare it as a registed hook. - A ``entry-points`` in ``pyproject.toml``, which pointing to the hookimp function. The subdomain of the entry-point is the ``PROJECT_NAME`` you can find in :ref:`manager`. View latest extension example on `GitHub `_. Plugin-supported modules ------------------------ - :ref:`API Reference for extended Data Connector `: :ref:`Data Connector ` is used to connect to data sources. - :ref:`API Reference for extended Cacher for DataLoader `: :ref:`Cacher ` is used for improving performance, reducing network overhead and support large datasets. - :ref:`API Reference for extended Data Processor `: :ref:`Data Processor ` is used to pre-process and post-process data. It is useful for business logic. - :ref:`API Reference for extended Inspector for Metadata `: :ref:`Inspector ` is used to extract metadata such as patterns, types, etc. from raw data. - :ref:`API Reference for extended Model `: :ref:`Model `, the model fitted by processed data and used to generate synthetic data. - :ref:`API Reference for extended Data Exporter `: :ref:`Data Exporter ` is used to export data to somewhere. Use it in CLI or library way to save your processed data or synthetic data.