Data Virtualization at Scale: Production Architecture

Real World Scale: At FAANG scale companies, data virtualization complements rather than replaces warehouses and data lakes. Consider an e-commerce company with a complex data landscape: 3 regional transactional databases handling 5,000 to 10,000 writes per second each, a CRM SaaS system, a Snowflake or BigQuery warehouse with 500 TB of historical events, and object storage ingesting 5 TB of clickstream logs daily. The business demands a Customer 360 API and real time dashboards. Product teams need single customer lookups with p50 latency under 200ms and p99 under 800ms. Analytics needs dashboards refreshing in under 2 to 5 seconds at p95 while joining across multiple systems. Three Layer Architecture: Production systems use a layered approach. First, the connection layer contains specialized adapters for each source, handling authentication, pagination, query dialect translation, and statistics collection. Each connector tracks average response times and row counts to feed the optimizer. Second, the abstraction and semantic layer defines logical models and business entities. This layer maps virtual "Customer" or "Order" schemas onto fragmented physical sources. It includes calculated fields, data quality rules, and standardized business definitions shared across teams. When marketing and finance both query "revenue," they get the same definition despite different underlying tables. Third, the consumption layer provides SQL interfaces for BI tools, APIs for services, and domain specific query languages. This layer enforces row and column level security, data masking, and auditing. Even if a source database has weak access controls, the virtual layer ensures compliance policies are consistently applied. Hybrid Materialization Strategy: Most large organizations end up with a hybrid pattern. They materialize the most expensive query patterns as views in the warehouse or lakehouse, achieving 10x to 50x performance gains for heavy analytical scans. Less critical aggregations or rarely queried datasets remain purely virtual, avoiding storage costs and keeping data fresh. Operationally, teams monitor per source error rates, p95 and p99 latencies, cache hit ratios, and query plans that frequently time out. Admission control and query quotas protect fragile backends like SaaS APIs from being overwhelmed by unexpected dashboard popularity.