Associate Data Practitioner

Evaluate Data Sharing Policies

Before sharing data using Analytics Hub, you must carefully evaluate several key factors. The most important considerations are data sensitivity, regulatory requirements, the target audience, and the potential business value. Sensitive data, like personal information or business secrets, needs strict controls to prevent harm if it is disclosed. You also need to understand who will use the data, as this determines the right level of access and security.

Ensuring regulatory compliance is a critical step. Different laws, like GDPR or HIPAA, may apply depending on the data and where the people accessing it are located. For example, sharing data internationally can expose you to different legal obligations. The platform itself has rules, such as requiring both the data publisher and subscriber to be in supported regions for certain marketplace transactions.

You should also weigh the business value of sharing data against the risks. Benefits might include better analytics, new revenue from selling data, or improved efficiency. However, you must balance these advantages against potential security threats and compliance challenges. A good policy safeguards sensitive information while following the law, allowing you to maximize the data's benefit safely.

Examine the Architecture of Data Exchanges and Listings

Analytics Hub organizes data sharing through a structured system. The core components are data exchanges, listings, and linked datasets. A data exchange is like a curated marketplace for data. Inside an exchange, a listing is a discoverable entry for a specific dataset, which users can subscribe to.

A major architectural benefit is the zero-copy infrastructure. When you share data through Analytics Hub, the data itself is not copied or moved. Subscribers get access to the original data in its storage location. This means subscribers are not charged for storing the data; they only pay for the computing resources used to query it. This setup provides real-time data access across different organizations without the cost and complexity of data replication.

The BigQuery service is the primary interface for querying this shared data. To help manage metadata across different systems, the BigLake Metastore offers a unified, serverless way to handle table definitions. This allows the same data to be queried by different engines, like Apache Spark and BigQuery, without extra configuration. Together, these components let you join external shared data with your own internal data for complete analysis.

Analyze Criteria for Data Sharing

Deciding when to share data using Analytics Hub involves analyzing specific criteria. First, classify your data by its sensitivity level, such as public, internal, or confidential. You should never share data that violates legal rules or your organization's own policies. Checking compliance requirements like GDPR or HIPAA is a necessary step before granting any access.

Next, evaluate the audience requirements. Determine who needs the data: is it an internal team, a partner company, or the general public? Analytics Hub lets you control subscriptions at the project or folder level. You should limit the sharing scope only to those who genuinely need it, which reduces the risk of unnecessary data exposure.

Finally, align sharing with your organizational policies and configure the right access controls. Key controls include IAM permissions (using roles like bigquery.dataViewer to control who can view data), encryption with customer-managed encryption keys (CMEK) for sensitive records, and enabling audit logs to track who accessed what data and when. In summary, share data only when it meets all these criteria—sensitivity, compliance, audience, and policy—and use Analytics Hub's features like private exchanges to maintain control.