Industry · Telecom & Networks

Data Engineering for Telecom

Network telemetry at carrier scale — 1B+ hourly events, sub-second anomaly detection, CDR pipelines, and churn analytics built on Apache Flink, Kafka, and ClickHouse.

Problems We Solve

Industry Challenges

Telemetry volume breaks batch thinking

Network elements emit billions of events per hour; by the time batch jobs land, the incident is over. Our Flink + ClickHouse NOC platform processes 1B+ hourly telemetry events with sub-second anomaly detection.

CDRs are big, messy, and regulated

Call detail records arrive in vendor-specific formats at enormous volume, and regulators expect retention and auditability. We build normalised, partitioned CDR pipelines with lineage and retention policies as code.

Churn shows up in the data first

Dropped sessions, degraded QoE, and billing disputes precede cancellations. We engineer feature pipelines that turn network and billing signals into churn-model-ready datasets.

OSS/BSS silos hide the customer

Network (OSS) and business (BSS) systems rarely share keys. We unify them into one governed model so engineering, care, and marketing see the same subscriber.

Proven In Production

Measured Results

1B+
Hourly telemetry events
apache flink + clickhouse
<1s
Anomaly detection
streaming, not batch
24/7
NOC-grade availability
carrier production
Evidence

Related Case Studies

Questions, Answered

Telecom FAQ

Can you really process a billion events per hour?
Yes — in production: an Apache Flink + Kafka ingestion layer feeding ClickHouse, sustaining 1B+ hourly network telemetry events with sub-second anomaly detection for NOC dashboards.
Which network data sources do you handle?
SNMP and streaming telemetry (gNMI), syslog, NetFlow/IPFIX, CDRs/EDRs from switching and charging systems, probe data, and OSS inventories — normalised onto common element and subscriber keys.
How do you keep storage costs sane at this volume?
Hot/warm/cold tiering: recent data in ClickHouse for interactive queries, history in object-storage lakehouse tables, aggressive columnar compression, and TTL policies — interactive speed where it matters, archive economics where it doesn't.
Can the same platform feed ML use cases like churn prediction?
Yes — the streaming layer doubles as a feature pipeline: QoE indicators, usage trends, and care interactions delivered point-in-time-correct to your data science team.
Get Started

Build Your Telecom Data Platform

Talk to a senior engineer who has shipped in your industry. Response within 24 hours.

Talk to an Engineer → View All Case Studies