# clickhouse Open-source column-oriented database management system for real-time analytical queries. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## September 2026 newsletter DevFeed: [September 2026 newsletter]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-17T13:42:49Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [releases](), [Replication](), [Grafana Cloud Metrics]() Tags: [2026](), [caching](), [clickhouse](), [events](), [news](), [newsletter](), [real-time](), [release](), [replication](), [september-2026]() ### AI overview The September 2026 ClickHouse newsletter covers the 26.8 release, including custom HTTP handlers, pipelined SQL, dynamic query filtering, tokenizer support, and other features. It also summarizes replication-queue diagnostics, CostBench results, preview releases, community contributions, and upcoming events. ### Source excerpt Welcome to the September 2026 ClickHouse newsletter, featuring ClickHouse 26.8, PromQL, On-Demand Compute, CostBench results, and the latest community news and events. ## ClickHouse is now available on the dbt platform DevFeed: [ClickHouse is now available on the dbt platform]() Original publisher: [Read original article]() Author: Aditya Chidurala; José Muñoz; Alex Francoeur Published: 2026-09-16T19:38:40Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Rust](), [Open Source]() Tags: [adapter](), [announcements](), [beta](), [clickhouse](), [cloud](), [dbt](), [open-source](), [platform](), [release](), [rust]() ### AI overview ClickHouse announces a dbt v2 adapter powered by dbt's Rust engine, available in public beta. ClickHouse is also available as a natively supported data warehouse on dbt Platform in private beta, with support for open-source ClickHouse and ClickHouse Cloud. ### Source excerpt The ClickHouse adapter for dbt v2 is in public beta, powered by dbt's Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud. ## ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine DevFeed: [ClickHouse Cloud Announces Private Preview of PromQL Support and Time-Series Table Engine]() Original publisher: [Read original article]() Author: James Cunningham Published: 2026-09-15T14:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Prometheus](), [Time Series](), [Cloud](), [Grafana](), [telemetry](), [Traces]() Tags: [clickhouse](), [cloud](), [grafana](), [logs](), [metrics](), [prometheus](), [time-series](), [traces]() ### AI overview ClickHouse announces a private preview of PromQL support and a time-series table engine in ClickHouse Cloud, allowing Prometheus metrics to be stored in ClickHouse and queried with existing PromQL. ### Source excerpt ClickHouse PromQL support lets you store Prometheus metrics in ClickHouse Cloud, query them using familiar PromQL, and bring metrics together with your logs and traces without rewriting queries in SQL. ## Replica-aware routing public beta DevFeed: [Replica-aware routing public beta]() Original publisher: [Read original article]() Author: Amy Chen; Jan Mensch Published: 2026-09-15T13:15:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Routing (disambiguation)](), [Replication](), [Caching]() Tags: [cache](), [clickhouse](), [consistency](), [replication](), [routing]() ### AI overview ClickHouse introduces replica-aware routing in Public Beta for Enterprise customers. The feature routes requests to the same replica, allowing continued access to temporary tables and named sessions and supporting read-after-write consistency over HTTP or the native protocol. ### Source excerpt Temporary tables and named sessions live on a single ClickHouse replica, so a follow-up query routed elsewhere can't see them. Replica-aware routing pins your requests to the same replica over HTTP or the native protocol -- and here's how we built it. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Introducing WalShadow: Sub-second Postgres replication to ClickHouse from physical WAL DevFeed: [Introducing WalShadow: Sub-second Postgres replication to ClickHouse from physical WAL]() Original publisher: [Read original article]() Author: Sai Srirampur Published: 2026-09-10T15:53:42Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Database](), [schema-evolution](), [Latency]() Tags: [analytics](), [architecture](), [benchmarks](), [clickhouse](), [github](), [latency](), [open-source](), [postgres](), [replication](), [schema-evolution](), [transactions]() ### AI overview WalShadow is an open-source engine that replicates PostgreSQL data to ClickHouse from the physical WAL stream. The article describes benchmark results of about 200 ms visibility latency and 289K rows per second, plus support for initial loads, continuous replication, schema evolution, recovery, and source switchovers. ### Source excerpt WalShadow replicates Postgres data directly from physical WAL into ClickHouse, delivering around 200 ms latency and 289,000 rows per second in benchmarks. ## Announcing On-Demand Compute: Instant compute for your most intensive workloads DevFeed: [Announcing On-Demand Compute: Instant compute for your most intensive workloads]() Original publisher: [Read original article]() Author: Melvyn Peignon Published: 2026-09-10T14:22:58Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [autoscaling](), [cloud-infrastructure]() Tags: [autoscaling](), [clickhouse](), [cloud](), [compute](), [cost](), [infrastructure](), [production](), [scale](), [workers]() ### AI overview ClickHouse announces a private preview of On-Demand Compute, which allocates shared workers to eligible individual queries so intensive workloads can run without competing with a service's primary compute. ### Source excerpt ClickHouse On-Demand Compute lets you scale individual queries with additional workers, run intensive workloads without disrupting production, and use compute when you need it. ## ClickHouse release 26.8 DevFeed: [ClickHouse release 26.8]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [parquet](), [data]() Tags: [bug](), [clickhouse](), [data](), [feature](), [parquet](), [performance](), [release](), [sql](), [tokenizers]() ### AI overview ClickHouse 26.8 is an LTS release that adds background queries, pipelined SQL, text tokenizers, and expanded data lake integrations, alongside performance improvements for Parquet, aggregations, and joins. ### Source excerpt ClickHouse 26.8 LTS introduces background queries, pipelined SQL, new text tokenizers, expanded data lake integrations, and faster Parquet, aggregation, and join queries. ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## Announcing ClickHouse Managed Postgres on Google Cloud DevFeed: [Announcing ClickHouse Managed Postgres on Google Cloud]() Original publisher: [Read original article]() Author: Kunal Gupta Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Google](), [Databases](), [cloud-infrastructure]() Tags: [analytics](), [clickhouse](), [gcp](), [google-cloud](), [postgres](), [replication]() ### AI overview ClickHouse announces a private preview of its managed Postgres service on Google Cloud, with NVMe-backed storage, native CDC into ClickHouse, and pg_clickhouse for a unified query layer. ### Source excerpt ClickHouse Managed Postgres is expanding to Google Cloud, bringing NVMe-backed storage, native CDC into ClickHouse, and a unified query layer via pg_clickhouse to GCP private preview customers. ## Introducing chdb Postgres extension: High-performance imports from cloud storage DevFeed: [Introducing chdb Postgres extension: High-performance imports from cloud storage]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-09-08T15:42:52Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [cloud-infrastructure](), [Amazon S3](), [parquet](), [CSV]() Tags: [benchmark](), [clickhouse](), [cloud](), [duckdb](), [extension](), [json](), [parquet](), [performance](), [postgres](), [s3](), [storage]() ### AI overview chdb is a new Postgres extension that uses the in-process ClickHouse engine to import and export data across cloud storage systems and formats. The article presents import benchmarks, format support, and usage through a query function and a COPY hook module. ### Source excerpt The chdb Postgres extension brings fast imports and exports across cloud storage platforms and data formats, powered by the embedded ClickHouse engine. ## Pipelined SQL in ClickHouse 26.8 DevFeed: [Pipelined SQL in ClickHouse 26.8]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-04T12:16:04Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Databases]() Tags: [clickhouse](), [feature](), [learn](), [sql]() ### AI overview ClickHouse 26.8 introduces pipelined SQL, which expresses a query as sequential transformations. The article compares conventional, FROM-first, and pipelined queries and explains that pipelines are translated to optimized standard SQL before execution. ### Source excerpt Learn how ClickHouse 26.8's pipelined SQL syntax lets you build multi-stage queries as a readable sequence of transformations. ## Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API DevFeed: [Managing ClickHouse Dedicated Clusters Through Tinybird's Organization UI and API]() Original publisher: [Read original article]() Author: Aitana Azcona Published: 2026-09-03T15:00:00Z Content type: release Language: en Sources: [Tinybird]() Topics: [clickhouse](), [ui](), [API]() Tags: [api](), [clickhouse](), [product-updates](), [ui]() ### AI overview Tinybird provides controls to observe workload signals, resize Dedicated clusters, and rebalance traffic through its Organization UI or API while it operates ClickHouse. ### Source excerpt Observe workload signals, resize Dedicated clusters, and rebalance traffic from the Organization UI or API while Tinybird operates ClickHouse. ## MySQL CDC connector for ClickPipes is now Generally Available DevFeed: [MySQL CDC connector for ClickPipes is now Generally Available]() Original publisher: [Read original article]() Author: Marta Paes Published: 2026-09-02T14:03:04Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [Infrastructure as code](), [Terraform]() Tags: [analytics](), [api](), [clickhouse](), [cloud](), [data](), [infrastructure-as-code](), [mysql](), [observability](), [release](), [terraform]() ### AI overview ClickHouse announces general availability of its MySQL CDC connector for ClickPipes, which replicates MySQL and MariaDB data into ClickHouse Cloud. The release adds automatic parallel snapshotting, reliability and observability improvements, and configuration through the Cloud API and Terraform. ### Source excerpt Replicate MySQL and MariaDB data into ClickHouse Cloud with the generally available MySQL CDC connector, featuring faster parallel snapshots, safer production defaults, improved observability, and infrastructure-as-code support. ## Using coding agents on a migration: Three practices that mattered DevFeed: [Using coding agents on a migration: Three practices that mattered]() Original publisher: [Read original article]() Author: Chandler Ortman Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [migration](), [clickhouse](), [AI-assisted coding](), [coding](), [configuration](), [Deployment](), [Environment Variables]() Tags: [cleanup](), [clickhouse](), [coding-agents](), [deployment](), [environment-variables](), [migration](), [prompt](), [pull-requests](), [temporal-voices]() ### AI overview The article examines coding agents used during a migration of Temporal Cloud usage and billing data to ClickHouse. It finds that the agents were most useful for cleanup and removal work, with durable cleanup instructions and exhaustive searches helping make that work practical. ### Source excerpt Three lessons from using coding agents during a ClickHouse migration, from cleanup automation to writing durable instructions that stay useful over time. ## From Neon Postgres to ClickHouse Managed Postgres DevFeed: [From Neon Postgres to ClickHouse Managed Postgres]() Original publisher: [Read original article]() Author: Sai Srirampur Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [migration](), [Latency]() Tags: [analytics](), [clickhouse](), [latency](), [migration](), [performance](), [postgres](), [production]() ### AI overview Three teams describe migrating production Postgres workloads from Neon to ClickHouse Managed Postgres. They cite reliability, performance, operational, and cost concerns, and report completing data migrations with ClickPipes in hours. ### Source excerpt Three teams share why they migrated production Postgres workloads from Neon to ClickHouse Managed Postgres and how ClickPipes helped them cut over in hours. ## ClickHouse welcomes RunReveal DevFeed: [ClickHouse welcomes RunReveal]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-01T22:01:39Z Content type: news Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [SIEM, Security](), [Database](), [telemetry](), [agent observability]() Tags: [ai-agents](), [clickhouse](), [database](), [logs](), [security](), [telemetry]() ### AI overview ClickHouse announces its acquisition of RunReveal, a security data platform built on ClickHouse. The article discusses security-data workloads, continued RunReveal support, and plans to apply the team's expertise to security and agentic investigations. ### Source excerpt Security is the largest and fastest growing data workload in the enterprise, and today, ClickHouse has acquired RunReveal. ## How Uken Games reduces observability costs by 87% with ClickHouse DevFeed: [How Uken Games reduces observability costs by 87% with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-01T15:28:24Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [telemetry](), [Traces](), [Grafana]() Tags: [clickhouse](), [cost](), [observability](), [opentelemetry](), [traces]() ### AI overview Uken Games replaced Datadog with an open-source observability stack centered on ClickHouse, cutting observability costs by 87%. The stack stores traces in ClickHouse and uses OpenTelemetry collectors with SigNoz, Prometheus, and Grafana. ### Source excerpt Uken Games replaced Datadog with an open-source observability stack built on ClickHouse, cutting costs by 87% while storing every trace on a single node. ## A new getting started experience for ClickHouse Managed Postgres DevFeed: [A new getting started experience for ClickHouse Managed Postgres]() Original publisher: [Read original article]() Author: Cristina Albu; Yashpreet Bathla Published: 2026-08-28T12:55:15Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Provisioning](), [real-time](), [Replication](), [Databases](), [Command-line interface]() Tags: [analytics](), [cli](), [clickhouse](), [cloud](), [data](), [docs](), [getting-started](), [guide](), [onboarding](), [postgres](), [real-time](), [replication]() ### AI overview ClickHouse Managed Postgres introduces a four-step onboarding experience that guides users from provisioning a Postgres service to loading data, querying it, and replicating it to ClickHouse for real-time analytics. ### Source excerpt See how a four-step onboarding flow helps users move from provisioning ClickHouse Managed Postgres to querying operational data and running real-time analytics with ClickHouse. ## So, is ClickHouse winning the observability wars? DevFeed: [So, is ClickHouse winning the observability wars?]() Original publisher: [Read original article]() Author: Mike Shi Published: 2026-08-27T18:24:53Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [telemetry](), [Database](), [anthropic](), [Netflix](), [OpenAI]() Tags: [anthropic](), [architecture](), [clickhouse](), [database](), [engineering](), [logs](), [metrics](), [netflix](), [observability](), [openai](), [saas](), [storage](), [telemetry](), [traces]() ### AI overview The article examines claims that ClickHouse is "winning the observability wars," focusing on its role as a storage and query engine for telemetry, especially logs and traces. It argues that success at the database layer does not by itself solve collection, schema design, correlation, visualization, alerting, investigation workflows, or broader observability product decisions. ### Source excerpt After claims that ClickHouse is "winning the observability wars" sparked debate, we reflect on why it has become a leading storage and query engine, where it still falls short, and why winning the database layer isn't the same as winning observability. ## A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres DevFeed: [A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Cloud](), [Kafka](), [databricks](), [API]() Tags: [api](), [clickhouse](), [cloud](), [data](), [databricks](), [house](), [latency](), [low-latency](), [tech-lead]() ### AI overview QuintoAndar rebuilt its customer data platform on ClickHouse Cloud, consolidating roughly 900 million monthly events from 14 million users and serving about 300 million API requests. The team replaced separate low-latency and batch-processing pipelines by writing events directly to ClickHouse. ClickHouse Managed Postgres provided compatibility with Hightouch and other Postgres-compatible tools. ### Source excerpt QuintoAndar unified ~900 million monthly events in ClickHouse Cloud and used ClickHouse Managed Postgres to make that data accessible to Hightouch and any Postgres-compatible tool. ## Postgres + ClickHouse Architectural Patterns DevFeed: [Postgres + ClickHouse Architectural Patterns]() Original publisher: [Read original article]() Author: Agus Syafaat Published: 2026-08-26T08:08:43Z Content type: article Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [PostgreSQL](), [Databases](), [Architecture & Design](), [data-platforms](), [real-time](), [Transactions](), [Low Latency](), [dashboards]() Tags: [analytics](), [architecture](), [clickhouse](), [clustercontrol](), [database-general](), [hybrid-operations](), [latency](), [olap](), [postgres](), [postgresql](), [real-time](), [time](), [transactions]() ### AI overview This article explains architectural patterns that combine PostgreSQL and ClickHouse. PostgreSQL serves as the authoritative transactional system for OLTP workloads, while ClickHouse handles large-scale analytical queries and real-time analytics. Continuous Change Data Capture synchronization connects the systems and separates transactional and analytical workloads. ### Source excerpt The role of databases has shifted significantly as modern applications must deliver real-time analytics, dashboards, and machine learning alongside low-latency transaction processing. Handling these diverse demands with a single relational database has become unsustainable under growing data volumes. Consequently, organizations are adopting specialized database architectures where multiple engines work together based on their strengths, allowing [...] The post Postgres + ClickHouse Architectural Patterns appeared first on Severalnines. ## What's new in the ClickHouse .NET Driver: the road from 1.0 to 1.3 DevFeed: [What's new in the ClickHouse .NET Driver: the road from 1.0 to 1.3]() Original publisher: [Read original article]() Author: Alex Soffronow Pagonidis Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [.NET](), [data](), [configuration](), [observability](), [debugging]() Tags: [api](), [clickhouse](), [community](), [debugging](), [ecosystem](), [integrations](), [logs](), [net](), [observability](), [performance](), [pull-request](), [types](), [workflows]() ### AI overview The ClickHouse .NET Driver progressed from version 1.0 to 1.3 with type-safe POCO workflows, broader type support, improved serialization and mapping, performance improvements, configurable behavior, observability, and integrations across the .NET ecosystem. ### Source excerpt Discover how ClickHouse .NET Driver 1.1-1.3 adds type-safe POCO workflows, extensible serialization, broader type support, performance improvements, and official ecosystem integrations. ## How Suprema Gaming made its data platform agent-ready with ClickHouse Cloud DevFeed: [How Suprema Gaming made its data platform agent-ready with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-25T18:52:53Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [migration](), [real-time](), [Cloud](), [data](), [Latency](), [Database](), [Artificial Intelligence](), [Amazon S3](), [Amazon Simple Queue Service (SQS)]() Tags: [agentic](), [ai](), [ai-agents](), [analytics](), [b2b](), [brazil](), [clickhouse](), [cloud](), [data](), [database](), [latency](), [migration](), [modernization](), [real-time](), [s3]() ### AI overview Suprema Gaming migrated its analytics platform from Snowflake to ClickHouse Cloud to support agentic operations. The move reduced warehouse costs by 62%, cut query latency from minutes to milliseconds, and improved data freshness from four hours to real time. The company now has an early production deployment of ClickHouse Agents serving teams across the group. ### Source excerpt Suprema Gaming migrated its analytics platform from Snowflake to ClickHouse Cloud to power a company-wide shift toward agentic operations. [Next page]()