metad 0.0.1

A hierarchical representation for the structure of a relational database.

An open source project from Data to AI Lab at MIT.

MetaData

This project aims to formally define a JSON schema which captures the structure of a relational database.

• JSON Schema: https://data-dev.github.io/MetaData/schema.html
• Documentation: https://data-dev.github.io/MetaData
• Homepage: https://github.com/data-dev/MetaData

Install

Requirements

MetaData has been developed and tested on Python 3.5, 3.6, 3.7 and 3.8

Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system in which MetaData is run.

These are the minimum commands needed to create a virtualenv using python3.6 for MetaData:

pip install virtualenv
virtualenv -p $(which python3.6) MetaData-venv

Afterwards, you have to execute this command to activate the virtualenv:

source MetaData-venv/bin/activate

Remember to execute it every time you start a new console to work on MetaData!

Install from source

With your virtualenv activated, you can clone the repository and install it from source by running make install on the stable branch:

git clone [e-mail]:data-dev/MetaData.git
cd MetaData
git checkout stable
make install

Install for Development

If you want to contribute to the project, a few more steps are required to make the project ready for development.

Please head to the Contributing Guide for more details about this process.

Quickstart

In this short tutorial we will guide you through a series of steps that will help you getting started with MetaData.

Creating Metadata Objects

You can also help create Metadata objects from scratch. The following code will create a MetaData object, add a table, and then save it to a JSON file.

from metad import MetaData

metadata = MetaData()

metadata.add_table({
    "name": "users",
    "primary_key": "id",
    "fields": [
        {"name": "id", "data_type": "id"},
        {"name": "name", "data_type": "text"}
    ],
})

Then, to export this object to a JSON file, you can run the following:

metadata.to_json("your_metadata.json")

Validating JSON Files

The core functionality of this library is to validate JSON files. The following code will load the metadata file for the hello_world dataset and validate it.

from metad import MetaData

metadata = MetaData.from_json("examples/hello_world/metadata.json")
metadata.validate()

What's next?

For more details about MetaData and all its possibilities and features, please check the documentation site.

History

0.0.1 (2020-05-22)

• First release on PyPI.