Building Modern Data Lakehouse Architecture for Telecom
The telecommunications industry generates massive volumes of data daily through CDRs, network logs, and customer interactions. Traditional data warehouses struggle with this scale, while data lakes lack the structure needed for business analytics. Enter the Data Lakehouse - combining the best of both worlds.
Why Data Lakehouse for Telecom?
Telecom operators face unique challenges:
- Volume: Billions of CDRs generated daily
- Velocity: Real-time fraud detection requirements
- Variety: Structured CDRs, unstructured logs, semi-structured JSON
- Veracity: Data quality issues from multiple network elements
Architecture Components
1. Storage Layer
Using object storage (Dell PowerStore) with Apache Iceberg format provides:
- ACID transactions
- Schema evolution
- Time travel capabilities
- Partition pruning
2. Processing Layer
Apache Spark and Flink handle:
- Batch processing for historical analytics
- Stream processing for real-time use cases
- ETL/ELT pipelines
3. Query Engine
Trino enables:
- SQL analytics across multiple data sources
- Federation with existing systems
- Sub-second query performance
Implementation Best Practices
- Partition Strategy: Partition CDR data by date and operator for optimal query performance
- Compaction: Regular file compaction to maintain query efficiency
- Data Retention: Implement tiered storage with hot/warm/cold data lifecycle
- Security: Row-level security for multi-tenant environments
Real-World Results
Our recent implementation for a major telecom operator achieved:
- 70% reduction in storage costs
- 10x improvement in query performance
- Real-time fraud detection with less than 1 minute latency
- Unified analytics across all data sources
Conclusion
Data Lakehouse architecture provides telecom operators with a modern, scalable foundation for analytics. By combining open-source technologies with telecom domain expertise, organizations can unlock the full value of their data assets.