DataSunrise is sponsoring RSA Conference2024 in San Francisco, please visit us in DataSunrise's booth #6178

Active Metadata

Active Metadata


In the world of data science and security, active metadata plays a crucial role. It is a powerful tool for managing and protecting sensitive data while ensuring effective data governance. This article will discuss active metadata, its importance, and how to handle it in software and programming languages.

Active metadata is data that changes and evolves over time. It is crucial for keeping information up-to-date and relevant. Managing active metadata involves using specific tools and techniques within software and programming languages.

What is Active Metadata?

Active metadata refers to the additional information attached to data that describes its characteristics, context, and usage. Unlike passive metadata, which remains static, active metadata is dynamic. You can update it in real-time. It provides valuable insights into the data’s origin, purpose, and sensitivity level.

The Importance of Active Metadata in Data Science

In data science, metadata is essential for several reasons:

  1. Data Discovery: Active metadata helps data scientists quickly locate and identify relevant datasets for analysis. It provides a clear understanding of the data’s structure, format, and relationships.
  2. Data Quality: By working with active metadata, data scientists can assess the quality and reliability of the data. It helps identify missing values, and potential errors.
  3. Data Lineage: Active metadata captures the data’s journey from its source to its current state. It records transformations, aggregations, and changes applied to the data, enabling data scientists to trace its lineage and understand its evolution.

Active Metadata and Data Security

Active metadata plays a vital role in data security and data governance. It helps organizations protect sensitive data by:

  1. Access Control: Active metadata can define access rights and permissions for different user roles. It ensures that only authorized individuals can access and modify sensitive data.
  2. Data Classification: By categorizing data based on its sensitivity level, metadata helps organizations apply appropriate security measures. It enables the implementation of data encryption, masking, and redaction techniques. We usually classify as sensitive a financial information and business decisions.
  3. Compliance: Metadata facilitates compliance with data privacy regulations such as GDPR and HIPAA. It allows organizations to track and monitor data usage, ensuring that sensitive information complies with legal requirements.

DataSunrise utilizes metadata analysis models to control all three of the subjects mentioned here. It includes security rules, audit and compliance components which control the sensitive data usage.

Handling Metadata in Software and Programming Languages

Let’s explore how you can handle active metadata in software and programming languages through examples.

Example 1: Python with PyArrow

Python, a popular programming language, offers a wide range of libraries and tools for data usage and analysis. One such library is PyArrow, which enhances Python’s capabilities by providing efficient handling of active metadata.

PyArrow is particularly useful for managing large datasets with complex metadata requirements. Users can store, find, and update metadata for their data, helping them keep track and manage information about the dataset.

Using PyArrow in Python helps users simplify data management and ensure accurate and accessible metadata. This can be especially beneficial for organizations dealing with vast amounts of data that require careful organization and tracking of metadata.

Overall, Python and PyArrow together provide a powerful solution for handling active metadata, enabling users to effectively manage and utilize their data resources. Here’s an example:

import pyarrow as pa
# Create a schema with metadata
schema = pa.schema([
pa.field("name", pa.string(), metadata = {"sensitivity": "high"}),
pa.field("age", pa.int32(), metadata = {"sensitivity": "low"})
# Create a table with metadata
data = [
{"name": "John Doe", "age": 30},
{"name": "Jane Smith", "age": 25}
table = pa.Table.from_pylist(data, schema=schema)
# Access metadata
name_metadata = table.schema.field("name").metadata
print(name_metadata) # Output: {'sensitivity': 'high'}

In this example, we define a schema with metadata using the metadata parameter. The name field is highly sensitive, while the age field has low sensitivity. We make a table with this plan and can see the current information of each section using the metadata feature.

Example 2: REST API with Apache Atlas

Apache Atlas is a powerful metadata management framework that allows users to effectively manage and organize metadata in Python-based applications. With Apache Atlas, users can easily capture, store, and analyze metadata to gain valuable insights into their data assets. This system assists users in tracking the history, ownership, and usage of data. It makes managing data assets in a company easier.

By providing a centralized platform for metadata management, Apache Atlas helps users improve data quality, ensure data governance, and enhance data discovery and collaboration. Apache Atlas is a useful tool for organizations wanting to improve how they manage metadata and utilize their data assets. Here’s an example:

import requests
import json

# Apache Atlas server URL
atlas_url = "http://localhost:21000"

# Authentication credentials
auth = ("admin", "admin")

# Create an entity with active metadata
entity = {
    "jsonClass": "org.apache.atlas.typesystem.json.InstanceSerialization$_Reference",
    "id": {
        "jsonClass": "org.apache.atlas.typesystem.json.InstanceSerialization$_Id",
        "id": "-1",
        "version": 0,
        "typeName": "customer",
        "state": "ACTIVE"
    "typeName": "customer",
    "values": {
        "name": "John Doe",
        "email": "[email protected]"
    "traitNames": [
    "traitAttributes": {
        "PII": {
            "sensitivity": "high"

# Create the entity with active metadata
response ="{atlas_url}/api/atlas/v2/entity", auth=auth, json=entity)
created_entity = response.json()

# Get the GUID of the created entity
entity_guid = created_entity["guidAssignments"]["customer"]

# Retrieve the entity and access active metadata
response = requests.get(f"{atlas_url}/api/atlas/v2/entity/guid/{entity_guid}", auth=auth)
retrieved_entity = response.json()

metadata = retrieved_entity["entity"]["classificationNames"][0]["attributes"]
print(metadata)  # Output: {'sensitivity': 'high'}

In this example, we use the requests library in Python to make HTTP requests to the Apache Atlas REST API. We assume that the Apache Atlas server is running on localhost with the default port 21000, and we use the default authentication credentials (admin, admin).

Atlas REST API step-by-step breakdown

We define the entity dictionary that represents the customer entity we want to create. It includes the entity’s type name (customer), attributes (name and email), and classification (PII) with metadata (sensitivity set to high).

We make a POST request to the /api/atlas/v2/entity endpoint to create the entity with active metadata. We pass the entity dictionary as the JSON payload and include the authentication credentials.

The response from the server contains the created entity, including the assigned GUID (Globally Unique Identifier). We extract the GUID from the response using created_entity[“guidAssignments”][“customer”].

To retrieve the created entity and access its active metadata, we make a GET request to the /api/atlas/v2/entity/guid/{entity_guid} endpoint, replacing {entity_guid} with the actual GUID obtained in the previous step.

The response from the server contains the retrieved entity, including its classifications. We access the metadata using retrieved_entity[“entity”][“classificationNames”][0][“attributes”].

Finally, we print the active metadata, which should output {‘sensitivity’: ‘high’}.

Summary and Conclusion

Active metadata is a fundamental concept in data science and security. It provides valuable information about data’s characteristics, context, and sensitivity, enabling effective data management and protection. By leveraging this concept, organizations can enhance data discovery, ensure data quality, maintain data lineage, and enforce data security and compliance.

We have observed how to control active metadata in software and programming languages. You can do this using Python with PyArrow and Apache Atlas. These examples demonstrate the ease and flexibility of including metadata into data workflows.

As data becomes increasingly complex and sensitive, the importance of metadata will continue to grow. Metadata practices can help data scientists and security professionals. They can maximize their data’s potential and protect it from unauthorized access and misuse.


Protection by Design

Protection by Design

Learn More

Need Our Support Team Help?

Our experts will be glad to answer your questions.

General information:
[email protected]
Customer Service and Technical Support:
Partnership and Alliance Inquiries:
[email protected]