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Real-Time Analytics
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October 2026 newsletter
The October 2026 ClickHouse newsletter announces CostBench results where ClickHouse Cloud delivered 752× better performance per dollar than Databricks and 438× to 512× better than BigQuery on real-time analytics workloads. The 26.9 release extends LIMIT with AFTER and UNTIL boundary conditions, adds incremental refresh for materialized views, and introduces CREATE TOKEN for time-limited credentials. Executable user-defined functions are now generally available on ClickHouse Cloud, supporting precompiled Rust, Go, C++, and JavaScript binaries with outbound network access and a deterministic flag. Featured community member Hoang Minh V has had over 100 pull requests merged since May, including a fast path for PromQL aggregations and fixes to TimeSeries reads. The newsletter also covers the official Apache Airflow provider for ClickHouse, the chDB durable layer for agent memory, and a Postgres round-up on memory safety and local NVMe storage.
Why Redshift Serverless can’t match ClickHouse Cloud for real-time analytics
CostBench streamed 113.2 billion stock-market quotes into both systems while four aggregate queries ran every ten minutes and two drill-down queries ran every hour. ClickHouse asynchronous inserts order raw data and build pre-aggregations during ingestion, while Redshift relies on separate background sorting and materialized view refreshes. ClickHouse Cloud led in preparation cost ($28.69 versus 522.03), normalized query cost ( 0.05 versus $24.97), and accumulated query runtime (58.12 seconds versus 1.69 hours). The combined score shows ClickHouse Cloud delivered 1,996× better end-to-end performance per dollar in this continuous-ingestion comparison. The difference originates in the preparation path: ClickHouse makes data query-ready during inserts, whereas Redshift’s streaming, sorting, and refresh steps advance independently.
Why BigQuery can’t match ClickHouse Cloud for real-time analytics
ClickHouse Cloud delivered 438× better end-to-end performance per dollar than BigQuery under Capacity pricing, and 512× under On-demand pricing, while continuously ingesting 113.2 billion stock-market quotes. The CostBench methodology measures preparation cost, query cost, and query runtime as data arrives. ClickHouse Cloud led in all three components: preparation cost was 8.5× lower than BigQuery Capacity, normalized query cost was 94× lower, and accumulated query runtime was 50.8× lower. The difference originates in how each system handles incoming data: ClickHouse orders raw data and builds current summaries during inserts, while BigQuery makes new rows queryable before background clustering and materialized view refresh complete. This leaves BigQuery aggregate queries to include rows that refresh has not yet summarized, increasing cost and runtime.
The lakehouse is a better data warehouse: 2026 benchmarks and proof
The data warehouse served structured SQL workloads for two decades, but the lakehouse now supports BI, data engineering, ML, streaming, and AI agents from a single governed platform. Databricks reports a 77% improvement in its production SQL workload mix since 2022, with customer migrations showing 90% faster queries (Lumen) and 80-90% faster analytics (Trek Bicycle). Lakehouse//RT delivers sub-second analytical queries at high concurrency directly on governed lakehouse tables, with Databricks reporting sub-100-ms latency at 12,000 queries per second, collapsing the traditional architecture of separate serving databases. The lakehouse eliminates data copying between systems, handles unstructured data, and runs on open formats like Delta Lake and Apache Iceberg, governed by Unity Catalog. Customer cost reductions range from 25% to 75%, with AXA Japan reducing warehouse costs by 60% and ETL costs by 70%, driven by eliminating redundant systems and data copies.
Why Databricks can’t match ClickHouse Cloud for real-time analytics
The CostBench benchmark streamed 113.2 billion stock-market quotes at one million rows per second while running aggregate and drill-down queries to compare ClickHouse Cloud and Databricks for real-time analytics under continuous ingestion. ClickHouse Cloud delivered 752× better end-to-end performance per dollar than the Databricks stack using Serverless SQL, Zerobus Ingest, liquid clustering, and incremental materialized views. ClickHouse Cloud led in all three components: preparation cost ($28.69 versus 695.60), normalized query cost ( 0.05 versus $2.65), and accumulated query runtime (56.39 seconds versus 29.10 minutes). The gap stems from ClickHouse preparing ordered raw data and current summaries on the insert path, while Databricks requires separate clustering and refresh services that cost more and can leave queries with unfinished layout work. Databricks’ new Lakehouse//RT, still in beta, was not included in this comparison.
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