“DAI-Lab” 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
License: MIT

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.

Install with pip¶

The easiest and recommended way to install MetaData is using pip:

pip install metad

This will pull and install the latest stable release from PyPi.

If you want to install from source or contribute to the project please read the Contributing Guide.

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({
    "id": "users",
    "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("your_metadata.json")
metadata.validate()

What’s next?¶

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