Transforming Telecom: How Cloud Computing, Queues & Caches Scaled a Tier-1 Telco’s Managed Services Operations

?In today's fast-paced digital landscape, managing massive amounts of data in real time is no longer optional—it's a necessity. A Tier-1 Telecom Operator faced the challenge of designing a unified Rating and Billing system capable of handling ove 60 Million subscribers and generating millions of Call Data Records (CDRs) daily while ensuring accurate billing, real-time responsiveness, and seamless integration with downstream systems. .

Here’s how we leveraged message queues and caches to build a scalable, fault-tolerant architecture that met these demands.

The Challenge: Real-Time Responsiveness Meets Scalability

The operator serves both prepaid (requiring real-time event-driven rating) and postpaid (asynchronous batch processing) customers. This dual requirement presented several challenges:

• Real-Time Prepaid Rating: Ensuring sub-10ms latency for balance updates while processing millions of events daily.
• Batch Postpaid Processing: Handling large volumes of data asynchronously without impacting system performance.
• Seamless Integration: Supporting downstream systems like Salesforce for account updates and external payment gateways for billing.
• Scalability & Fault Tolerance: Building a system that could scale with growing subscriber numbers while maintaining high availability.

The Solution: Message Queues and Caches at the Core

To address these challenges, we designed an architecture leveraging Kafka for message queuing and Redis for caching. Here’s how these components played a pivotal role across different layers of the system:

1. Ingestion Layer: Preprocessing Millions of CDRs

A mediation system was built to preprocess CDRs and push them into Kafka topics (cdr_prepaid for real-time processing and cdr_postpaid for batch processing). Leveraging Heroku PaaS, Kafka was used as a managed service, reducing operational complexity while ensuring scalability. A Heroku API Gateway was deployed for API-driven CDR ingestion, handling schema validation, authentication, and routing.

Trade-offs:

• Using Heroku’s managed API Gateway simplified deployment but came with cost overheads compared to a custom-built solution.
• Real-time schema validation ensured high data quality but introduced slight latency in the ingestion pipeline.

2. Processing Layer: Real-Time vs Batch Processing

At the heart of the architecture lies the processing layer, powered by Kafka consumers and Redis caching.

Prepaid Rating (Real-Time Processing):

A Kafka consumer processes prepaid CDRs in real time, integrating tightly with Redis for instant balance checks. Redis significantly improved response times by caching frequently accessed data such as consumer balances and subscription details. The flow was optimized to achieve sub-10ms latency for balance updates, ensuring a seamless experience for prepaid customers.

Postpaid Rating (Batch Processing):

For postpaid customers, a batch-processing service was developed using Heroku worker dynos. This service processed CDRs asynchronously from the cdr_postpaid topic. Billing data was persisted in Heroku Postgres, and events like BillGenerated were published back to Kafka for downstream systems.

Key Features:

• Redis caching reduced database load by 30%, enabling faster response times.
• TTL (Time to Live) policies ensured cached data remained fresh while minimizing stale data issues.

Trade-offs:

• Real-time prepaid rating added complexity but ensured responsiveness, while batch postpaid processing introduced latency for billing updates.
• Cache-aside patterns improved performance but required careful TTL tuning to prevent stale data.

3. Persistence & Integration Layer

The architecture seamlessly integrated with downstream systems:

• Heroku Postgres stored transactional data such as billing records.
• Salesforce was used to maintain customer records and trigger platform events.
• Kafka enabled reliable event-driven communication with external payment gateways, fraud detection systems, and notification services.

4. Monitoring & Observability

To ensure smooth operations across this complex system:

• Heroku Metrics, Prometheus, and Kafka monitoring tools were used to track system health (e.g., consumer lag, topic throughput).

Results: A Scalable, Fault-Tolerant Architecture

By leveraging message queues (Kafka) and caches (Redis), the operator achieved significant improvements in system performance and reliability:

1. Scalability: The event-driven architecture handled millions of CDRs daily without bottlenecks.
2. Real-Time Responsiveness: Sub-second response times were achieved for prepaid customers through optimized caching strategies.
3. Fault Tolerance: Kafka’s replication features ensure no data loss during peak loads or failures.
4. Database Load Reduction: Redis caching reduced database load by 30%, improving overall efficiency.
5. Seamless Integration: Kafka enabled reliable communication with downstream systems like Salesforce and payment gateways.

Key Trade-offs & Lessons Learned

While message queues and caches were instrumental in achieving these goals, several trade-offs had to be carefully managed:

1. Real-Time vs Batch Processing: Balancing responsiveness for prepaid customers with the scalability needs of batch postpaid processing required careful architectural decisions.
2. Managed Services vs Custom Solutions: Using Heroku’s managed services simplified development but increased operational costs compared to custom-built alternatives.
3. Consistency vs Performance: Cache invalidation strategies were critical to ensuring fresh data without sacrificing speed.
4. Event Ordering vs Parallelism: Kafka’s partitioning guaranteed event order for individual customers but limited parallelism for high-volume users.

Conclusion: Message Queues & Caches as Enablers of Modern Architecture

The success of this Tier-1 Telecom Operator’s unified Rating and Billing system highlights the crucial role that message queues and caches play in modern architectures. By combining the scalability and fault tolerance of Kafka with the performance optimization capabilities of Redis, they built a system that not only met current demands but also positioned them for future growth.

What are your thoughts on using message queues or caches in your architecture? Have you faced similar challenges? Let’s connect—I’d love to hear about your experiences!?