Project Name
Telecom CDR Analytics Rebuilt on StarRocks, 60% Less Report Latency and Sub-2-Second Queries
A telecom provider processing massive volumes of Call Detail Records had its analytics constrained by legacy batch ETL processes. High data latency and scalability issues meant CDR logs could not be processed in real time, leading to data staleness and significant reporting delays during peak traffic hours. Network operations teams had no instant visibility into call performance, usage patterns, or compliance metrics. Applying its AI-First approach, Ksolves built a scalable real-time analytics platform on StarRocks, replacing complex batch pipelines with Kafka stream ingestion, delivering sub-2-second query latency across 5 PB+ of CDR history, and enabling instant network insights without complex pre-aggregation.
- High Data Latency From Legacy Batch ETL: Hours of latency between CDR generation and analytical visibility. Network operations teams made decisions on stale data with no real-time response capability.
- Scalability Issues at 5M+ CDR Events Per Hour: Existing infrastructure created bottlenecks at peak CDR volumes, degrading query performance and introducing ingestion lag.
- Significant Reporting Delays During Peak Traffic: Reports required by network planning and compliance teams took significantly longer during peak hours, reducing operational responsiveness.
- Complex Batch Pipelines With No Real-Time Architecture: Fragile batch pipelines required significant engineering effort to maintain and could not serve real-time query requirements without a complete rebuild.
- No Instant Queries on Petabyte-Scale CDR History: Ad-hoc queries across petabytes of CDR history required complex pre-aggregation pipelines. Queries on the full dataset were impractical without pre-computed summaries.
- Data Staleness Impacting Decision-Making: Hours-old CDR data meant network operations, fraud teams, and compliance functions acted on information that no longer reflected current network state.
Ksolves implemented StarRocks as a unified OLAP engine ingesting directly from Apache Kafka, replacing complex batch pipelines with a real-time architecture. The governing principle: eliminate the gap between CDR generation and analytical visibility by ingesting, storing, and querying in one unified platform without complex pre-aggregation.
- Apache Kafka Real-Time CDR Ingestion: Kafka ingests 5M+ CDR events per hour directly into StarRocks, replacing batch ETL pipelines entirely. Real-time ingestion eliminates data staleness and ensures queries always reflect current network activity.
- StarRocks Unified OLAP Engine: 3 FE nodes for query routing and 24 CN nodes for compute and storage across 5 PB+ of CDR history. 10TB+ ingested daily at 99.95% data accuracy with sub-2-second query latency and 100+ concurrent analytical queries supported.
- Materialized Views for Instant Analytics: StarRocks Materialized Views provide pre-computed network performance and usage summaries updating automatically as new CDR data lands, enabling instant queries across petabytes without complex pre-aggregation pipelines.
- Real-Time BI Dashboards: Network operations, planning, and compliance teams get instant visibility into call performance, usage patterns, and regulatory metrics. Report generation is 3x faster with 60% reduction in overall report latency.
- Prometheus + Grafana Monitoring: Real-time pipeline health monitoring across the full stack. Zero pipeline failures recorded since deployment.
Technology Stack
| Category | Technology |
|---|---|
| Streaming | Apache Kafka |
| Analytics Warehouse | StarRocks (3 FE + 24 CN Nodes) |
| Aggregation | StarRocks Materialized Views |
| Visualization | BI Dashboards |
| Monitoring | Prometheus + Grafana |
- Report Latency Cut 60% - Hours to Seconds: Stream-based Kafka ingestion eliminated batch ETL latency. Report latency reduced 60% and data staleness eliminated. Network operations now act on current CDR data.
- Sub-2-Second Query Latency Across 5 PB+: StarRocks delivers under 2-second query latency across 5 PB+ of CDR history. 100+ concurrent queries supported without performance degradation.
- 3x Faster Report Generation: Materialized Views and StarRocks unified architecture deliver 3x faster reports compared to the legacy batch setup.
- 99.95% Data Accuracy: Stream ingestion with StarRocks high-availability architecture maintains 99.95% data accuracy across 10TB+ daily CDR ingestion.
- Zero Pipeline Failures Since Deployment: Simplified real-time architecture replacing complex batch pipelines has recorded zero pipeline failures. Operational reliability significantly improved.
- Massive Concurrency for Network Operations: StarRocks handles 100+ concurrent analytical queries across network operations, fraud analysis, and compliance simultaneously without resource contention.
“Our network operations team was making decisions on CDR data that was hours old. Every peak traffic event revealed how slow our batch pipeline was. StarRocks with Kafka ingestion gave us real-time visibility for the first time – sub-2-second queries across years of CDR history and reports that are 3x faster than anything our legacy system could produce.”
-Head of Network Analytics / CTO.
A telecom provider processing 5M+ CDR events per hour on a legacy batch ETL infrastructure, suffering hours of data latency and significant reporting delays, was transformed through Ksolves Big Data Services. StarRocks with Kafka stream ingestion replaced complex batch pipelines. 10TB+ ingested daily. Sub-2-second queries across 5 PB+. Report latency cut 60%. 3x faster reports. 99.95% data accuracy. Zero pipeline failures. 100+ concurrent queries. Network operations, fraud teams, and compliance functions now act on real-time CDR data.
Is Your Telecom CDR Analytics Platform Still Running Hours Behind Network Reality on Batch ETL?
