Telecom data, where the volume is the design constraint
Telecom databases are not ordinary databases with more rows in them. Call detail record volume changes which designs are viable at all, and retention rules mean the usual answer to growth — delete the old data — is frequently not available.
What changes here
Database Management in telecommunications is not the same engagement
Ingest that does not lose records under peak
Mediation pipelines fail at the peak, not the average, and a dropped record is revenue that will not be billed. We design for burst with backpressure and replay, so a downstream slowdown delays processing rather than losing it.
Partitioning and tiering as a first-class decision
At CDR volumes, partition strategy determines whether a query returns in seconds or never. Storage tiering keeps the retained years affordable without making them unqueryable when a dispute or a regulator asks.
Reconciliation between mediation, rating, and billing
The failure that costs real money is silent divergence between what was mediated, what was rated, and what was billed. Reconciliation is built as a standing control with alerting, not as a monthly report someone reads.
The rules that govern telecom data retention
In most industries retention policy is about cost. Here it is about obligations that point in opposite directions, and the architecture has to satisfy both.
- Minimum retention for billing dispute and regulatory purposes, often measured in years
- Maximum retention under privacy obligations, which pulls the other way
- CPNI restrictions on who may access usage data and under what circumstances
- Lawful intercept and preservation requests that override ordinary deletion
- Auditable deletion — being able to prove data went away, not merely that it was scheduled to
The work itself
Full database management pagePerformance tuning and query optimization
Slow-query analysis, index and plan work, connection pooling, and caching — measured against your real workload rather than a synthetic benchmark.
Migrations, upgrades, and platform moves
Version upgrades, on-premise to cloud, and engine changes run with dual-write or replication, a verified row count, and a rollback path.
Schema design and data modelling
Normalization where integrity matters and denormalization where reads do, with constraints and migrations version-controlled alongside the application.
High availability and replication
Primary/replica topologies, automatic failover, and read scaling, with the failover path exercised rather than documented and forgotten.
Backup, retention, and tested restore
Point-in-time recovery, retention set against your obligations, and restore drills that produce an actual number for how long it takes.
Monitoring and managed DBA cover
Query, lock, replication-lag, and capacity alerting, with a named engineer for the escalations that arrive at 2am.
Telecommunications questions we get asked
Something more specific? Send us the situation and we’ll answer it straight.