Authored by Bharath Suresh, Senior Partner Sales Engineer, Snowflake
Foundations of Multi-Cloud Data Governance
Businesses are increasingly adopting multi-cloud data lake and lakehouse strategies to gain deeper insights. They leverage cloud platforms offering scalable computing, resilient storage, and versatile tools to process large data assets. This approach simplifies technological complexity, accelerates time to market, and provides a strategic advantage for advanced analytics and ML/AI projects.
The multi-cloud landscape spans various cloud service provider (CSP) regions, ensuring flexibility, redundancy, and optimized performance. Effective data governance ensures data assets' consistency, quality, and security while adhering to compliance and regulatory requirements. Robust frameworks help manage complexity, mitigate risks, and build trust in decision-making.
Challenges of Data Governance in Multi-Cloud Environments
However, Achieving governance across multiple clouds presents significant challenges, including security, compliance, performance, and operational consistency across diverse platforms. Some key challenges arise due to the following -
- Cross-Cloud Platform Inconsistencies: Complex multi-cloud environments with inconsistent tool sets, policy definition scope, security controls, varying data classification techniques, and access control configs create uniformity and integration challenges.
- Data Silos & Fragmentation: Diverse and complex data collaboration environments create vulnerabilities, complicating the management of cross-platforms, creating risks associated with data transfer and storage access across cloud environments.
- Compliance Complexity: Monitoring region-specific regulatory requirements, aligning with potentially conflicting laws, and maintaining consistent, auditable processes across diverse geographical and legal landscapes create significant governance challenges and increase the risk of non-compliance.
- Data Proliferation: Rapid data growth, multiple data formats, and maintaining consistent data lifecycle management across disparate environments introduce challenges related to security breaches.
- Challenges with Open Table Formats and Interoperability: The emergence of open-table formats such as Apache Iceberg, Delta, and Apache Hudi enables seamless data movement and interoperability across processing engines and cloud environments. However, this flexibility also increases complexities. The flexibility creates challenges in versioning, tracking, lineage, and role-based access control (RBAC).
For example, managing consistent encryption and Role-Based access control (RBAC) policies becomes challenging when the same Delta table is accessed from both AWS EMR (using AWS KMS for encryption) and Google Dataproc (using Cloud KMS). Similarly, ensuring that IAM roles and permissions are properly synchronized across both cloud platforms while maintaining the principle of least privilege becomes complex.
These challenges underscore the critical need for a platform with advanced toolsets and seamless connectivity and collaboration capabilities to govern the complexities of data in multi-cloud environments effectively. In the next section, we will see how Snowflake addresses these challenges.
The adoption of data governance should consider key pillars, including the following:
Key Pillars of Data Governance:
- Policies and Compliance: Clear implementation of data usage, privacy, and security policies to safeguard data and ensure compliance with various regulations. Data Quality: Continuous monitoring of Key Quality Indicators (KQIs) for data integrity, reliability, relevance, completeness, and readiness for analytics.
- Data Discovery and Lineage: Allows users to search, discover, and document data relationships. Data lineage helps users understand data origin, context, and business impact.
- Metadata Management: Implement a centralized metadata collation for a unified view of enterprise data, enhancing discoverability, profiling, and classification.
- Data Privacy and Security: To safeguard sensitive information, ensure fundamental security measures, such as data masking, encryption, and access controls.
- Collaboration and Democratization: Provide self-service capabilities to users for efficiently accessing the data they need across roles, fostering collaboration and transparency.
- Monitoring and Optimization: Ability to track processes, data quality, and privacy metrics, along with audit trails and remediation workflows, governance integrity, and support continuous improvement.
- People, Processes, and Technology: Effective governance integrates skilled personnel, robust processes, and advanced tools—such as data profiling, lineage tracking, and automation—to ensure alignment and scalability.
By focusing on these pillars, organizations can build a robust data governance framework to minimize risks, ensure compliance, and achieve lasting success.
Data Governance with Snowflake’s Horizon Catalog
Snowflake 101 is a multi-cloud Software-as-a-Service (SaaS) data platform that operates seamlessly across AWS, Microsoft Azure, and Google Cloud. Its cloud-agnostic architecture allows organizations to store, process, and analyze data.
Snowflake's Horizon Catalog Snowflake Horizon Catalog is a built-in governance and discovery solution for the Snowflake AI Data Cloud. It helps stakeholders, such as data governors, security admins, CISOs, data engineers, and AI/ML teams, discover and govern data, apps, models, and more across their clouds. With Snowflake Horizon, teams can resolve cross-cloud security risks, apply governance protections, and enable data teams to discover, access, and share governed data, apps, and models.
Horizon Catalog provides governance capabilities for the following:
- Data, apps, and models within Snowflake's organizational accounts.
- Organization Data including open-table format Apache Iceberg from external sources.
- Marketplace Public and Private listings.
- Snowflake Native Apps and Data sets from the Snowflake Marketplace.
- Third-party applications and systems are integrated via connectors.
Horizon Catalog transforms fragmented data environments into a streamlined, integrated, managed ecosystem, enabling organizations to confidently navigate the intricate landscape of multi-cloud data governance.
For instance, a global enterprise can use Horizon Catalog to centrally manage and monitor data access patterns across their AWS deployments in North America, GCP instances in Europe, and Azure workloads in Asia-Pacific, all from a single interface. This unified view enables them to enforce consistent governance policies, track data lineage, and maintain compliance requirements across their entire multi-cloud ecosystem without switching between different cloud-specific tools or consoles. Let's take a closer look at the features.
Key Functionalities of Snowflake Horizon
Compliance
Snowflake Horizon provides automated compliance tracking, real-time monitoring rules, and audit trails. Organizations can track object dependencies, audit access history, monitor data quality with custom metrics, and visualize data lineage for consistent adherence to regulatory requirements.
Security
Ability to implement multi-layered security with advanced features such as:
- Threat detection and data encryption for advanced protection.
- Granular access controls, including column- and row-level protections.
- PII identification and data masking are used to safeguard sensitive information.
- Central Trust Center for continuous risk monitoring based on industry benchmarks.
Privacy
Ensures controlled access and data protection through:
- Aggregation and projection policies to manage data visibility.
- Anonymization and pseudonymization techniques to protect sensitive data.
- Snowflake Data Clean Rooms (DCRs) for secure, multi-organization collaboration.
- Differential privacy to safeguard against advanced privacy attacks while enabling data usability.
Discovery
Simplifies data discovery, including:
- Universal Search for natural language discovery of data, apps, and models.
- Automated metadata management and semantic classification to track data origins and usage.
- AI-powered object description tagging for better context and discoverability.
- An Internal Marketplace and Snowflake Marketplace for organizational and public data sharing and collaboration.
Collaboration
The platform enhances secure data sharing and coordination through:
- Controlled access and granular permissions for collaboration within governance boundaries.
- Workflow tools for seamless data management.
- Features like private listings, unified billing, and self-service trials streamline data sharing across cloud regions without duplication or ETL processes.
Conclusion
In the face of growing complexities in multi-cloud data environments, effective governance is no longer optional—it's essential. Snowflake Horizon Catalog empowers organizations with the tools, strategies, and insights to unify fragmented ecosystems, safeguard sensitive data, and maintain compliance. By addressing critical governance pillars such as security, privacy, compliance, and collaboration, Snowflake provides a transformative solution for navigating the intricate landscape of multi-cloud data management. With Snowflake Horizon, organizations can turn data governance challenges into opportunities, unlocking the full potential of their data assets while maintaining trust, transparency, and operational excellence.
