ClickHouse for telecom CDR and network analytics

ClickHouse is presented as a suitable platform for telecom analytics at very high CDR and session-record volumes because its columnar MergeTree storage, compression, partitioning, and aggregation-oriented query model support fast analysis of append-only network data. The recommended architecture separates voice, SMS, data-session, signaling, and radio-probe records into dedicated fact tables, normalizes vendor feeds through mediation, and ingests data primarily through Kafka microbatches or batch loads rather than single-row inserts. Schema and sort-key design should reflect distinct access patterns, with NOC dashboards optimized around region, service, result, and time while subscriber-centric queries may require separate tables. The approach supports operational KPIs such as drop rates, answer-seizure ratios, attach failures, congestion correlation, APN behavior, interconnect reconciliation, fraud detection, and roaming analysis, while materialized hourly rollups and table-specific retention policies preserve longer-term reporting without scanning raw data. Key operational concerns include monitoring ingest and Kafka lag, preventing excessive table parts, handling mediation replays through versioned deduplication, avoiding FINAL for real-time serving, and validating rollup parity and query pruning before production use. The piece also describes Tinybird as a managed ClickHouse option that combines streaming ingestion, SQL transformations, authenticated APIs, schema migration workflows, and monitoring for teams seeking to reduce cluster-management responsibilities.

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