Data Warehousing for Telecom: Modeled for CDR Volume and Network Reality

A telecom data warehouse modernization runs into a problem the project scope didn't anticipate: a mid-size carrier generates billions of CDRs per day across voice, SMS, data, and signaling. The cloud warehouse the architect chose handles the schema fine, but the compute cost of querying CDR-level data turns out to be 4x what the business case projected. The team tries pre-aggregation. The aggregations break the analytical use cases that need raw CDR detail for fraud analysis and customer-level usage reconciliation. The team tries selective sampling. The samples miss the fraud patterns that matter. The team tries time-series partitioning. It helps but doesn't solve the cost problem. The warehouse goes live but the analytics team uses it sparingly because every query costs money the FinOps team noticed. None of this would have happened if the dimensional model and the storage layer had been designed for telecom CDR scale from the start.

Telecom data warehousing done right starts with the CDR volume reality. Dimensional models with proper grain — usually session-level or aggregated by hour for some dimensions, full CDR for others. Compression and columnar storage chosen for the access patterns. Retention policies that move older data to cheaper tiers. Query patterns designed around clustering and partitioning. Cost monitoring that catches expensive queries before they become billing surprises. With these in place, telecom warehousing is affordable at any operator scale. Without them, it produces a platform that works technically but costs more than the value it produces.