# olap Published articles for olap. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments DevFeed: [ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments]() Original publisher: [Read original article]() Author: Kyle Buzzell Published: 2026-08-05T08:38:18Z Content type: release Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [Cloud](), [on-prem](), [Databases]() Tags: [analytics](), [clickhouse](), [cloud](), [clustercontrol](), [clustercontrol-releases](), [clustercontrol-v2-5-0](), [database](), [olap](), [on-prem](), [release](), [sovereign-dbaas]() ### AI overview ClusterControl 2.5.0 adds support for ClickHouse across on-premises, cloud, and hybrid environments. The release enables deployment and lifecycle management for standalone or replicated ClickHouse clusters, alongside PostgreSQL backup workflow upgrades, usage metering, and a restored ClusterControl v1 capability. ### Source excerpt ClusterControl 2.5.0 is here, and it marks a milestone for the platform: ClickHouse joins the family of supported database engines -- and with it, a capability no analytics vendor's cloud can offer you. Wherever you run ClusterControl -- on-premises, in any cloud, or hybrid -- you can now deploy ClickHouse either as a standalone OLAP [...] The post ClusterControl 2.5.0 brings ClickHouse support to on-prem, cloud and hybrid environments appeared first on Severalnines. ## From OLTP to OLAP: 7 ClickHouse® Optimization Mistakes DevFeed: [From OLTP to OLAP: 7 ClickHouse® Optimization Mistakes]() Original publisher: [Read original article]() Author: Iago Enríquez Published: 2026-08-03T10:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [clickhouse](), [Optimization]() Tags: [clickhouse](), [engineering-excellence](), [mistakes](), [olap](), [optimization]() ### AI overview A practical article about seven ClickHouse optimization mistakes encountered when moving from OLTP to OLAP workloads. ### Source excerpt Practical lessons from optimizing analytical workloads in ClickHouse®. ## Agentic kill switch is a database problem. So we built Redpanda SQL DevFeed: [Agentic kill switch is a database problem. So we built Redpanda SQL]() Original publisher: [Read original article]() Author: Alexander Gallego Published: 2026-08-03T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Databases](), [SQL](), [stream-processing](), [Google Cloud Platform (GCP)](), [Amazon Web Services](), [systems]() Tags: [agentic](), [announcement](), [aws](), [cloud](), [code](), [database](), [exploit](), [google-cloud](), [governance](), [models](), [olap](), [real-time](), [scale](), [sql](), [stream-processing](), [systems](), [tools]() ### AI overview The article presents Redpanda SQL as a database built for agentic governance. It describes real-time bridge queries, efficient SQL JOINs, a ring-buffer shuffle algorithm, and analytical processing designed to detect agent behavior and provide an agentic kill switch. Redpanda SQL is stated to be available on AWS and Google Cloud. ### Source excerpt Agentic governance needs a new kind of database, so we built Redpanda SQL. Now available on both AWS and Google Cloud. ## Building a Modern Analytics Stack Around ClickHouse DevFeed: [Building a Modern Analytics Stack Around ClickHouse]() Original publisher: [Read original article]() Author: Sebastian Insausti Published: 2026-07-01T10:35:21Z Content type: article Language: en Sources: [SeveralNines]() Topics: [clickhouse](), [Databases](), [Open Source](), [MySQL](), [PostgreSQL](), [real-time](), [NVMe](), [Replication](), [IO]() Tags: [analytics](), [analytics-stack](), [clickhouse](), [cta](), [database-general](), [databases](), [distributed](), [mysql](), [nvme](), [olap](), [open-source](), [postgresql](), [real-time]() ### AI overview This article explains how ClickHouse fits into a modern analytics stack alongside relational databases, Redis, and Prometheus. It describes ClickHouse as an open-source column-oriented DBMS for analytical workloads and discusses columnar storage, distributed processing, replication, and near-real-time ingestion. ### Source excerpt Historically, relational databases did double duty. The same PostgreSQL / MySQL instance that handled your application's writes also answered your business questions. A well-indexed schema, a few GROUP BY reports, done. And that works, right up until it doesn't: the reports get slower, the dashboards start eating the same I/O budget as the application; or, [...] The post Building a Modern Analytics Stack Around ClickHouse appeared first on Severalnines. ## What I shipped in my first 60 days at Mintlify DevFeed: [What I shipped in my first 60 days at Mintlify]() Original publisher: [Read original article]() Author: Dens Sumesh Published: 2025-11-06T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [systems](), [clickhouse](), [Kafka](), [olap](), [AI Agent](), [Artificial Intelligence]() Tags: [ai](), [ai-agent](), [analytics](), [clickhouse](), [engineering](), [infrastructure](), [kafka](), [olap]() ### AI overview An engineer describes work shipped during the first 60 days at Mintlify, including a custom analytics platform built with Kafka and ClickHouse, an AI agent for querying analytics data, and an abuse checker for filtering customer feedback. ### Source excerpt Building analytics infrastructure, abuse detection systems, and caching solutions. ## 10x speed, 10x savings: Constant Contact's journey from Pinot to ClickHouse DevFeed: [10x speed, 10x savings: Constant Contact's journey from Pinot to ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-11-04T07:39:45Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data](), [dashboards](), [Databases]() Tags: [amazon](), [analytics](), [analytics-stack](), [apache](), [clickhouse](), [dashboards](), [olap](), [pinot](), [real-time](), [streaming]() ### AI overview Constant Contact rebuilt its real-time analytics stack by migrating from Apache Pinot to ClickHouse. The article describes the scaling and maintenance problems caused by Pinot's handling of upserts and growing data volumes, and explains how ClickHouse fit the team's OLAP and reporting requirements. ### Source excerpt Constant Contact rebuilt its real-time analytics stack with ClickHouse. In this blog post, they share their lessons doing so. ## Why HTAP Databases Are Giving Way to Disaggregated Architectures DevFeed: [Why HTAP Databases Are Giving Way to Disaggregated Architectures]() Original publisher: [Read original article]() Author: Zhou Sun Published: 2025-05-04T10:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [olap](), [Cloud](), [Latency](), [consistency](), [NoSQL](), [MongoDB](), [Hadoop](), [hdfs](), [CockroachDB](), [Amazon Redshift](), [vitess]() Tags: [cloud](), [cloud-data](), [cockroachdb](), [consistency](), [data-lake](), [databases](), [engineering](), [hadoop](), [hdfs](), [latency](), [mongodb](), [olap](), [redshift](), [sql](), [vitess]() ### AI overview The article traces the separation of transactional and analytical database workloads, explaining how differing storage and scaling requirements led to specialized OLTP and OLAP systems. It argues that HTAP as a single database architecture is declining while its underlying ideas persist in today's disaggregated data stack. ### Source excerpt This blog is inspired by Jordan Tigani's "Big Data is Dead." Jordan and I actually spent some time building an HTAP database at SingleStore. From the one database that did everything in the '80s, to the great divide, to HTAP, to today's disaggregated stack--here's why HTAP as a database is dead, but its spirit lives on. ## Outgrowing Postgres: optimize and integrate OLTP + OLAP stack DevFeed: [Outgrowing Postgres: optimize and integrate OLTP + OLAP stack]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-27T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [olap](), [Transactions]() Tags: [analytics](), [how-to](), [olap](), [postgres](), [scale](), [the-data-base]() ### AI overview This tutorial explains how to optimize Postgres for analytics and when to integrate an OLAP system alongside its transaction-processing workload. ### Source excerpt Postgres handles transactions fine but chokes on analytics at scale. Here's how to optimize it and when to add OLAP. ## Outgrowing Postgres: how to evaluate the right OLAP solution DevFeed: [Outgrowing Postgres: how to evaluate the right OLAP solution]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-17T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [olap](), [postgres](), [the-data-base]() ### AI overview This article discusses how to evaluate an OLAP solution when outgrowing Postgres for analytics, emphasizing the importance of understanding query patterns. ### Source excerpt Outgrowing Postgres for analytics? Evaluating the right OLAP solution requires understanding your query patterns. Start here. ## Outgrowing Postgres: When to move OLAP workloads off Postgres DevFeed: [Outgrowing Postgres: When to move OLAP workloads off Postgres]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-02-07T00:00:00Z Content type: article Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [olap](), [postgres](), [the-data-base]() ### AI overview The article explains when to move analytics workloads off Postgres by identifying technical and team health warning signs before a crisis. ### Source excerpt Learn when to move analytics off Postgres by watching for technical and team health warning signs before crisis hits. ## Outgrowing Postgres: How to run OLAP workloads on Postgres DevFeed: [Outgrowing Postgres: How to run OLAP workloads on Postgres]() Original publisher: [Read original article]() Author: Ariel Pérez Published: 2025-01-31T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [olap]() Tags: [analytics](), [deep-dive](), [olap](), [postgres](), [techniques](), [the-data-base]() ### AI overview A deep dive into running OLAP workloads on Postgres, covering basic optimizations, advanced techniques, and when to consider moving away from Postgres. ### Source excerpt A deep dive into running analytics on Postgres, from basic optimizations to advanced techniques and knowing when to quit. ## Postgres Meets Analytics: CDC From Neon to ClickHouse Via PeerDB DevFeed: [Postgres Meets Analytics: CDC From Neon to ClickHouse Via PeerDB]() Original publisher: [Read original article]() Author: Sai Srirampur Published: 2024-10-02T16:39:48Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [clickhouse](), [Databases](), [real-time](), [Data analysis](), [Replication](), [data](), [autoscaling](), [web applications](), [Serverless](), [Open Source]() Tags: [analysis](), [analytics](), [autoscaling](), [clickhouse](), [code](), [community](), [data](), [databases](), [latency](), [low-latency](), [olap](), [open-source](), [performance](), [postgres](), [real-time](), [replication](), [warehouse](), [web-applications]() ### AI overview This article explains how Neon serverless Postgres and ClickHouse can be combined for transactional workloads and real-time analytics. It presents PeerDB's Change Data Capture and replication capabilities as the bridge for synchronizing Neon data to ClickHouse for customer-facing analytics and data warehousing. ### Source excerpt If you're building a data-driven application that handles large amounts of data, you may need to balance two different types of databases: a purpose-built operational DB for your transactions, and an analytical database for large-scale data analysis. A purpose-built analytical da... ## SQL vs NoSQL Explained DevFeed: [SQL vs NoSQL Explained]() Original publisher: [Read original article]() Author: Nelson Djalo Published: 2024-07-02T16:00:57Z Content type: comparison Language: en Sources: [Amigoscode Newsletter]() Topics: [NoSQL](), [SQL](), [Databases](), [clickhouse]() Tags: [acid](), [business-intelligence](), [consistency](), [data-analysis](), [database](), [database-scalability](), [durability](), [microsoft](), [mysql](), [nosql](), [olap](), [oracle](), [postgresql](), [rdbms](), [relational-database-management-systems-rdbms](), [relational-databases](), [schema](), [sql]() ### AI overview This comparison explains the differences between SQL and NoSQL databases, including their characteristics, scalability models, and common use cases. It covers SQL database features such as structured schemas and ACID compliance, along with relational and OLAP database categories. ### Source excerpt Choosing the Right Database for Your Needs