The Complete Guide to Multi-Platform Data Sharing

In today's data-driven economy, enterprises need to share data with clients, partners, and subsidiaries across multiple platforms. However, building custom data pipelines for each client and platform combination quickly becomes a maintenance nightmare.

The Challenge: Platform Fragmentation

Different clients have different preferences and infrastructure setups:

• Enterprise clients often prefer Snowflake Data Shares for real-time access
• Analytics teams may request Databricks Delta Sharing for ML workflows
• Legacy systems still require traditional sFTP file transfers
• Compliance requirements vary by client and industry

Building separate infrastructure for each combination leads to:

• High engineering overhead
• Inconsistent security controls
• Difficult change management
• Operational complexity

The Solution: Unified Control Plane

A modern approach consolidates all data sharing operations through a single control plane that:

1. Source Connectivity

Connect once to your primary data sources:

• Snowflake warehouses
• Databricks clusters
• PostgreSQL databases
• Cloud storage (S3, Azure Blob, GCS)

2. Governance Layer

Apply consistent controls across all destinations:

• Row-level security filters
• Column masking and encryption
• Client-specific access policies
• Audit logging and compliance tracking

3. Multi-Destination Delivery

Automatically deliver to client preferences:

• Snowflake Data Share: Zero-copy sharing for real-time access
• Databricks Delta Sharing: Native integration with Delta Lake
• sFTP: Encrypted file transfers for legacy systems

Implementation Best Practices

Security First

• Implement least-privilege access controls
• Use client-specific encryption keys
• Maintain detailed audit trails
• Regular access reviews and revocation

Operational Excellence

• Automate schema change propagation
• Monitor delivery success rates
• Set up alerting for failures
• Document client-specific requirements

Performance Optimization

• Cache frequently accessed datasets
• Optimize data formats per platform
• Implement intelligent retry logic
• Monitor and tune delivery times

Getting Started

1. Assess Current State: Document existing data sharing arrangements
2. Define Governance: Establish security and compliance requirements
3. Choose Architecture: Select unified platform vs custom build
4. Pilot Implementation: Start with 2-3 key clients
5. Scale Gradually: Migrate remaining clients systematically

Conclusion

Multi-platform data sharing doesn't have to mean multi-platform complexity. By implementing a unified control plane with proper governance, you can deliver data to any platform while maintaining security, compliance, and operational efficiency.

The key is to abstract away platform differences while preserving the native benefits each platform provides to your clients.