cid cc679a1c18bc6f554bdd5aba8a081d477ea4258fb7e7db9d905ab1872bdd6fac · original · open access · via openalex
Title: ClickHouse for High-Volume ML and Statistical Observability Authors: Cendryva Venue: Zenodo (CERN European Organization for Nuclear Research) Year: 2026 Canonical: doi:10.5281/zenodo.20404126 URL: https://doi.org/10.5281/zenodo.20404126 Collector: openalex OA: true ABSTRACT This white paper explains how ClickHouse can serve as the analytical backbone for high-volume ML observability, statistical monitoring, and operational intelligence. Modern organizations need to analyze large volumes of model scores, feature freshness records, sensor events, product telemetry, customer journeys, infrastructure signals, and operational metrics. These workloads are time-oriented, append-heavy, and aggregation-driven, making column-oriented analytics a strong fit. The paper covers observability schema design, rollups, materialized views, analytical storage patterns, high-throughput ingestion, query performance, retention strategy, and common implementation mistakes. It also explains how Cendryva uses ClickHouse-style analytical patterns to support model telemetry, condition history, anomaly inspection, freshness monitoring, dashboards, and executive health views for high-event-volume environments. Canonical version: https://cendryva.com/whitepapers/clickhouse-high-volume-observability/ Keywords: ClickHouse observability, ML observability, statistical monitoring, high-volume analytics, operational telemetry, anomaly detection, production AI monitoring, Cendryva.