AI Data Registry

* More Developers Docs:

The AI Data Registry module provides a structured and robust system for maintaining a registry of datasets with associated metadata. It allows organizations to track key details about datasets used in AI/ML pipelines, ensuring reproducibility, traceability, and compliance with governance requirements.

The DataCatalog class is critical for managing datasets, storing metadata, and facilitating searches or audits in large data pipelines. Tasks such as loading, saving, and updating dataset logs are automated for seamless integration into workflows.

The corresponding ai_data_registry.html covers GUI-driven interactions and use cases for dataset registration, visual cataloging, and metadata export for regulatory purposes.

With this module, you can:

• Record key attributes of datasets, such as size, source, and creation time.
• Automatically maintain dataset metadata in persistent storage.
• Improve governance by enabling quick lookups, audits, and metadata management.

Dataset governance and traceability are essential for modern AI and data science workflows. The DataCatalog class offers an automated system for maintaining a persistent dataset registry, enabling easier compliance with data governance policies like GDPR, HIPAA, and ISO 27001.

It allows data teams to:

1. Track dataset metadata: Record details about where datasets come from, their size, and their relevance.
2. Enable reproducibility: Ensure consistent references to data used in experiments and predictions.
3. Streamline audits: Provide a reliable catalog for inspections and audits, ensuring compliance with data policies.

No matter what domain you're working in, a centralized data registry ensures better data management throughout the lifecycle of analytics and machine learning workflows.

The ai_data_registry.py module is designed to:

• Provide a simple yet effective mechanism for registering and querying datasets and metadata.

• Ensure retrieval of standardized information about datasets, including their structure, size, and source.

• Assist with automation in ETL workflows, ensuring datasets are persistently tracked across transformations and pipes.

• Facilitate compliance and governance by maintaining comprehensive dataset logs, metadata, and time records.

1. Large-scale AI/ML systems where multiple datasets are processed and managed at different stages.
2. Regulatory domains requiring reliable recordkeeping for traceability.
3. Collaborative environments where datasets are shared among teams.

The DataCatalog module provides the following key features:

• Dataset Registration:
  1. Easily add datasets to the registry with custom metadata.
  2. Automatically tags datasets with date_added timestamps.

• Persistent Storage:
  1. Datasets and metadata are saved in JSON format for reliability and readability.
  2. Supports a default catalog file (data_registry.json) or custom file paths.

• Automated Logging:
  1. Provides comprehensive logs for dataset updates, saving actions, and errors.
  2. Stores execution details for ease during audits.

• Error Handling:
  1. Recovers from failures, such as file access issues or malformed JSON files.

• Extensive Metadata Support:
  1. Accepts custom metadata fields, enabling users to store domain-specific attributes for datasets.

• Integration-Friendly Design:
  1. Can be seamlessly integrated into pipelines for efficient governance across AI/ML workflows.

The DataCatalog class provides three main functions for dataset registry management:

• add_entry: Adds a dataset with metadata to the registry.
• load_catalog: Loads the existing catalog stored in a local JSON file.
• save_catalog: Saves or updates the registry, ensuring persistent storage.

The add_entry method accepts a dataset name and metadata as inputs. Metadata can be any dictionary describing meaningful attributes (e.g., size, source, dependencies).

Example Metadata Record:

json
{
    "dataset_name": {
        "metadata": {
            "source": "internal API",
            "size": "1.2 GB",
            "format": "CSV"
        },
        "date_added": "2023-10-27T12:34:56"
    }
}

The following examples demonstrate how to use DataCatalog for basic and advanced use cases.

Add a dataset with metadata and manage the registry.

python
from ai_data_registry import DataCatalog

# Initialize the catalog

catalog = DataCatalog()

# Add a dataset entry

catalog.add_entry("customer_data", {
    "source": "internal API",
    "size": "1.2 GB",
    "format": "CSV"
})

# Load catalog data

data = catalog.load_catalog()
print("Current Registry:", data)

You can add more intricate metadata, such as tags and related datasets, to help track high-level attributes of your datasets.

catalog.add_entry("model_training_data", {
    "source": "AWS S3",
    "tags": ["training", "version:1.0"],
    "related_datasets": ["raw_data_v1", "processed_data_v1"],
    "records": 1000000
})

—

By default, the DataCatalog saves the registry in data_registry.json. You can configure it to use a different file path when needed.

# Initialize the catalog with a custom file path

catalog = DataCatalog(registry_path="/tmp/custom_data_registry.json")

# Add an entry to the catalog

catalog.add_entry("experiment_data", {
    "source": "API",
    "size": "5 GB"
})

# Output the catalog contents

print(catalog.load_catalog())

Use this feature to store registries separately for better organization in multi-pipeline projects.

—

Combine DataCatalog with versioning capabilities. This allows you to track the progress of specific dataset versions directly in your pipeline.

def versioned_entry(catalog, dataset_name, version, **kwargs):
    """
    Add a versioned dataset entry to the catalog.

    :param catalog: Instance of DataCatalog
    :param dataset_name: The dataset name
    :param version: Dataset version (e.g., "v1.0.0")
    :param kwargs: Additional metadata fields
    """
    metadata = {
        "version": version,
        "source": kwargs.get("source", "unknown"),
        "size": kwargs.get("size", "unknown"),
    }
    catalog.add_entry(dataset_name, metadata)

# Initialize catalog

catalog = DataCatalog()

# Add a versioned entry

versioned_entry(
    catalog,
    "customer_data",
    version="v1.0.0",
    source="internal",
    size="2 GB"
)

# Output the catalog

print(catalog.load_catalog())

Expected Output:

{
    "customer_data": {
        "metadata": {
            "version": "v1.0.0",
            "source": "internal",
            "size": "2 GB"
        },
        "date_added": "2023-10-27T12:34:56"
    }
}

Use this feature to help track dataset versions at different stages of your data lifecycle.

—

To get the most out of DataCatalog, apply these best practices:

• Use metadata consistently Add fields like source, size, and tags to all datasets for uniformity.
• Secure the registry file Protect data_registry.json with proper file permissions to prevent unauthorized access.
• Version datasets Track changes over time using clear versioning (e.g., `v1.0.0`).
• Automate updates Use tools like Airflow or Prefect to keep the registry accurate and up to date.

The DataCatalog can be extended depending on your specific organizational needs. Here are some common ways to make it more useful:

• Search and Filter Utility:

Add functionality that allows users to search the registry for datasets based on criteria like tags, version, or metadata fields.

• Support Export Formats:

Enable exporting the entire catalog or parts of it as external formats, such as CSV, YAML, or database records. This allows for smoother integration into data pipelines.

• Validation Rules:

Add schema validation using `jsonschema` or similar libraries to ensure metadata consistency and accuracy before saving.

—

The DataCatalog module is a scalable and flexible solution for managing metadata registries. With support for versioning, extensibility, and pipeline integration, it ensures that complex workflows can maintain data reproducibility, traceability, and governance. Whether you’re working on small-scale or enterprise-level pipelines, the DataCatalog provides all the tools you need for clean and structured data management.