Predictive Network Analytics Platform for a National Telco
CYVERRA architected and deployed a cloud-native telemetry lakehouse and predictive streaming analytics platform across multi-vendor fixed and mobile networks. The solution ingests over 10TB of daily network telemetry to provide near-real-time network performance monitoring and automated capacity forecasting.
National broadband and mobile network operator
- -88%
- Telemetry processing latency
- 4.2x faster
- Query execution speed
- -35%
- Data infrastructure TCO
- 24 hours
- Congestion forecast lead time
The Challenge
The client operated disparate network environments spanning legacy 3G/4G RAN, emerging 5G deployments, and nationwide fibre infrastructure. Operational telemetry, probe data, and Call Detail Records (CDRs) were stored across isolated on-premises storage clusters, creating massive analytical silos. Legacy batch processing workflows introduced reporting latencies of up to six hours, preventing the Network Operations Centre (NOC) from detecting transient service degradations before they impacted subscribers. Furthermore, expanding hardware maintenance costs on legacy on-premise clusters made scaling the analytics infrastructure financially unsustainable.
Our Approach
CYVERRA engineered a scalable cloud lakehouse architecture using Apache Kafka and Apache Spark on Kubernetes for high-throughput streaming ingestion and transformation. Telemetry streams from cell towers, optical nodes, and core routing infrastructure were normalised into a unified schema stored in open table formats with automated data lifecycle tiering. We implemented modular dbt transformation pipelines and integrated a high-concurrency query engine to support ad-hoc analytics across both historical performance trends and streaming network events. In parallel, our team developed machine learning models to forecast regional cell-site congestion up to 24 hours in advance. To ensure enterprise security and compliance, CYVERRA implemented automated data masking and pseudonymisation on all subscriber-identifiable telemetry, accompanied by fine-grained role-based access control and Infrastructure as Code provisioning using Terraform.
The Outcome
The deployment transformed network operations from reactive troubleshooting to predictive maintenance. End-to-end data latency dropped from several hours to under four minutes, allowing operational teams to proactively reroute traffic and rebalance capacity prior to peak degradation periods. Migrating from legacy on-premises big data appliances to an elastic cloud architecture delivered significant cost efficiencies. The unified platform now serves as the single source of truth for both operational network engineering and commercial customer experience teams.
Technology used
- Apache Kafka
- Delta Lake
- Apache Spark
- dbt
- Trino
- Terraform
- Kubernetes
- Amazon Web Services
Consolidating our petabyte-scale telemetry into a single real-time cloud analytics platform transformed how our engineering teams identify and mitigate network congestion.