Observability data is hostile to general-purpose databases. Signals share a common profile: high write throughput, massive cardinality, time-series access patterns, and queries that scan billions of rows expecting sub-second answers. Most databases buckle under this combination. ClickHouse doesn't — but understanding why requires going deeper. In this session, we'll walk through the technical fundamentals that make ClickHouse uniquely suited for observability workloads: columnar storage and vectorised execution, the MergeTree family and TTL-based tiering, sparse indexing for time-range scans, and native ZSTD compression that routinely achieves 10–20× ratios on telemetry data. We will briefly touch the operational aspects of ClickHouse like shard topology and Replication. At the end of the session, the audience will leave with the takeaway of how to build and operate storage backend for modern observability data at scale.
Soham Chakraborty is a senior devops engineer at Sematext. He has spoken at conferences like Config Management Camp, DevOpsDays, SREDay, KCD among others. He also helps to organize local meetups and events and have previously worked as an organizer of AWS Community Day, Global Azure Kolkata among others Bikram Debnath is an Advisory Software Engineer at IBM Labs, 3× Patent Holder, and innovator in telemetry and distributed systems. Passionate about Observability, APM, IoT, and Automotive solutions. Skilled in Microservices, Kubernetes, Java, Kafka, and modern data stacks.
