Self Serve Platform: Standardized Infrastructure at Scale

The Platform Challenge: When you decentralize data ownership to 50 domains, you face a risk: each domain reinventing storage patterns, security configurations, and monitoring. This creates chaos. The self serve platform solves this by abstracting common concerns into standardized, automated capabilities that every domain uses. Core Platform Capabilities: The platform provides an event backbone for streaming, typically Kafka or similar at massive scale handling 100,000 to 500,000 events per second across all domains. It offers provisioning APIs where a domain declares "I need a partitioned table updated from this stream" and the platform spins up storage, compute, and access control automatically. Orchestration handles both batch and streaming workflows, scheduling transformations and ensuring dependencies run in the correct order. A unified metadata and catalog service is critical. Every data product registers here with schemas, lineage, SLOs, and documentation. When an analyst searches for payment data, they discover Payment Failed Events with full context: who owns it, what the schema is, what the freshness guarantee is, and who else uses it. Without this catalog at scale, you would have 200+ data products scattered across systems with no discoverability. Governance Embedded in Tooling: Federated governance defines global standards: naming conventions for fields like user_id versus customer_id, security policies for encryption and access, data classification levels (public, internal, confidential), and required quality checks. The key insight is these rules are not enforced by manual review. They are embedded in the platform. When a domain creates a new product exposing user identifiers, the platform automatically checks which fields are sensitive based on classification policies. It configures masking, restricts access to approved roles, and tags the product for compliance audits. If a domain tries to create a product with a retention period violating regulatory requirements (for example, keeping PII longer than allowed), the platform rejects it at provisioning time. This computational governance is what makes data mesh viable at scale. Manual governance through tickets and reviews does not scale to 200 data products and hundreds of engineers making changes daily. Integration Patterns: For existing systems, the platform supports multiple integration patterns. Change Data Capture (CDC) from monolithic databases can feed domain products with analytical freshness under 5 minutes end to end. Event sourcing from operational microservices enables near real time products with sub second latency for use cases like fraud detection. Where only batch APIs are available, scheduled pulls might have hourly or daily refresh, which affects downstream SLAs and must be documented in the product contract.