Telecom Data Lakehouse Architecture: Unifying CDR, OSS, and BSS Analytics at Scale

Telecommunications operators sit atop some of the largest and most complex data ecosystems on the planet. Every day, billions of call detail records (CDRs), network performance metrics from operations support systems (OSS), and customer transactions from business support systems (BSS) flood into disjointed data silos. The challenge is not merely volume—though petabyte-scale is standard—but integration: how to unify these diverse data streams into a single analytical foundation that drives both network optimization and business value. The emerging telecom data lakehouse architecture is answering this call, combining the scalability of data lakes with the governance and performance of data warehouses to transform how operators manage, analyze, and monetize their data assets.

Traditional telecom data architectures are fragmented along functional lines. Network teams maintain OSS data warehouses storing performance metrics, alarm logs, and configuration data—typically updated in near-real-time and queried for network operations. Business teams run BSS systems handling billing, CRM, and product catalogs, optimized for transactional consistency. CDR processing pipelines exist in their own world, processing billions of records daily for billing and revenue assurance. Each system uses different technologies, data models, and governance frameworks, making cross-domain analysis slow, expensive, and often impossible.

This fragmentation has real business costs. When a customer experiences a service degradation, the network team sees the technical alarm, the customer care team sees the complaint ticket, and the billing team sees the service credits—but no single team has the complete picture. Churn prediction models trained only on BSS data miss the early warning signals buried in network performance data. Revenue assurance teams struggle to reconcile CDR discrepancies with billing records, leaking millions in undetected revenue leakage. The data lakehouse approach addresses these problems by providing a unified analytics layer across all telecom data domains.

A telecom data lakehouse is built on three foundational layers, each addressing specific telecom data challenges.

The ingestion and storage layer handles the massive velocity and variety of telecom data. CDRs streaming at millions per second are ingested via real-time pipelines built on Kafka or Pulsar, landing in open table formats like Apache Iceberg or Delta Lake on object storage. OSS performance data—often time-series metrics from thousands of network elements—uses specialized time-series table layouts with optimized partitioning by time and network element ID. BSS data, typically slower-changing and structured, is batch-ingested from relational databases via CDC (Change Data Capture). The lakehouse's schema-on-read flexibility accommodates the messy, evolving nature of telecom data without forcing premature structuring.

The semantic and governance layer is where the lakehouse earns the "house" part of its name. Telecom-specific data models—common information models (CIM) or TM Forum SID frameworks—are implemented as SQL views and materialized tables on top of the raw lake data. Data catalogs with lineage tracking ensure that analysts can trace a KPI like "dropped call rate" from the dashboard all the way back to the raw CDR records and network element measurements. Access control is unified: network engineers see OSS performance data, billing analysts see financial data, and customer care representatives see a curated 360-degree customer view—all from the same underlying data.

The serving and analytics layer provides purpose-built query engines for different workloads. Interactive SQL queries for dashboarding run on Trino or Spark SQL, benefiting from lakehouse optimizations like data skipping and vectorized execution. Real-time streaming analytics for network anomaly detection uses Flink or Spark Structured Streaming, querying both real-time streams and historical lake data in a single pipeline. Machine learning workloads—churn prediction, fault prediction, capacity planning—access the lakehouse directly via Python APIs, eliminating the need to copy data into separate ML environments.

The unified data lakehouse enables use cases that were previously impractical or impossible. Proactive network management is perhaps the most impactful. By correlating real-time performance metrics with historical failure patterns and customer impact data, operators can predict network outages hours or days before they occur. A European carrier reported a 40% reduction in mean time to repair after implementing lakehouse-based predictive maintenance, as engineers could prioritize repairs based on both technical severity and predicted customer impact.

Revenue assurance and fraud detection improve dramatically with unified data. CDR records can be cross-referenced with billing data, roaming partner records, and customer profiles in near-real-time, catching billing errors and subscription fraud that would otherwise slip through siloed systems. One Asian operator identified $12 million annually in previously undetected revenue leakage within the first year of deploying their data lakehouse.

Customer 360 and personalized experiences become genuinely achievable. Instead of a fragmented view across billing, network, and support systems, the lakehouse provides a single, continuously updated customer profile. When a high-value customer calls support, the agent can immediately see their plan details, recent network experience, payment history, and product usage—enabling personalized resolutions that reduce churn and increase satisfaction.

Building a telecom data lakehouse is not without challenges. Data quality is a persistent issue: CDRs come from dozens of network elements with inconsistent formats; OSS alarms vary by vendor; BSS data carries decades of legacy system artifacts. Successful deployments invest heavily in data quality pipelines, schema validation, and master data management before making data available to consumers.

Performance at telecom scale requires careful optimization. Partitioning strategies must balance query patterns—CDR queries often filter by date and subscriber, while OSS queries filter by network element and time. Data clustering, zoning, and statistics collection are critical for query performance on multi-petabyte datasets. Operators should plan for incremental optimization, starting with the highest-value use cases and expanding as performance patterns emerge.

Regulatory compliance adds complexity. Telecom data is heavily regulated—call records are sensitive personal data, location data requires special protections, and data retention periods are mandated by law. The lakehouse architecture must include row-level security, data masking, automated retention policies, and audit logging as foundational features, not afterthoughts.

As 5G and 5.5G networks generate exponentially more data—from higher speeds, denser networks, and new IoT use cases—the case for a unified data lakehouse only strengthens. Operators that successfully implement this architecture are transforming themselves from connectivity providers into data-driven organizations. The data lakehouse is more than a technology upgrade; it's a foundational platform that enables the next wave of telecom innovation—from AI-powered network automation to new data monetization streams. In an industry where every millisecond of latency and every percentage point of churn matters, the ability to analyze all data, all the time, isn't just an advantage—it's a survival imperative.