Side Car Deployment

Side Car is a federated deployment model where partners centrally build and govern data, analytics, and AI assets in their own Databricks workspace, then securely deliver them to customer environments using Delta Sharing and Clean Rooms. Partners retain control over asset quality, versioning, and intellectual property, while customers maintain authority over their data and execution environment.

This pattern enables partners to deliver intelligence at scale without per-customer infrastructure management, particularly when consistency, auditability, and cross-organization collaboration are required without centralizing data ownership.

Common Use Cases

Managed ETL & Data Curation at Scale

Partners apply proprietary transformation, normalization, and enrichment logic to customer data, delivering clean, analytics-ready datasets while keeping pipeline implementations private and avoiding per-customer managed deployments.

Analytics & Metrics as a Service

Standardized business logic, calculations, and semantic definitions are applied consistently across customer datasets, producing trusted metrics and curated views that customers can consume directly in their own environments.

Governed Model Distribution & Execution

Validated analytics and AI models are shared with versioning and execution guarantees, enabling customers to run approved models locally while preserving partner intellectual property and ensuring consistent outcomes.

Audit, Validation & Controls as a Service

Proprietary audit logic, reconciliation rules, and control frameworks are applied to customer data under controlled access, producing certified results rather than raw data extracts.

Privacy-Conscious Multi-Party Analytics

Multiple organizations participate in shared analysis—such as benchmarking, fraud signal detection, or research—using tightly scoped access and controls that enable shared insights without unrestricted data exchange.

Enabled Through Delta Sharing

Delta Sharing provides secure, governed access to live data and AI assets across organizational boundaries, without requiring tight platform coupling or bespoke integrations.

When combined with Clean Rooms, it supports purpose-built collaboration where data access, transformation, and output are explicitly constrained by policy. Together, they enable organizations to move from ad hoc data exchange to repeatable, auditable intelligence delivery.

Centralized Governance with Flexible Execution

The Side Car does not require customers to run Databricks for compute, but it does rely on Databricks as the governance and intelligence control plane in most real-world deployments.

While open protocols enable broad participation, the Databricks-managed versions of Unity Catalog, Delta Sharing, MLflow, and Clean Rooms provide the enterprise-grade governance, auditability, and collaboration controls that the Side Car model depends on.

• Customers may consume shared datasets or outputs from non-Databricks systems
• Customers can run compute wherever it makes sense for them
• Databricks centralizes policy enforcement, asset governance, and validation
• Execution remains flexible and optional across customer environments

Pricing & Cost Model

The Side Car benefits from a low cost of entry due to Databricks' usage-based pricing model.

Included Capabilities

Core governance and control-plane capabilities are included at no additional cost:

• Workspaces
• Unity Catalog
• Delta Sharing
• MLflow

Charges are driven primarily by compute usage.

Typical Cost Allocation

Party	Pays For
Partner	Databricks compute used to build, govern, and publish intelligence assets
Customer	Data egress or downstream compute, depending on how assets are consumed

This model enables one-to-many scale while preserving strong governance and flexible execution choices.

What's Next

• Hybrid — Partner-managed with customer data plane
• Customer Managed — Customer-owned infrastructure
• Governance — Unity Catalog patterns for federated access