Blog

A walkthrough of self hosting an open source model such as GLM, connecting it to ClickHouse for agentic analytics, and the benefits around data locality, cost and privacy.

How to combine open source and open weight models such as Deepseek, GLM and Kimi with ClickHouse using third party inference providers like Fireworks.ai and Baseten.

Agentic analytics is a step beyond text-to-SQL — planning, decomposition and iteration over your data. Here's what it means in practice and why ClickHouse is a natural fit.

As companies execute their AI strategies, most databases can't keep up with the concurrency, freshness and price/performance that agentic workloads demand. Here's why ClickHouse is uniquely well suited.

Considerations when analysing the cost of ClickHouse.

How ClickHouse is entering buy side and sell side firms and being adopted for front office use cases.

Agentic analytics is changing how financial services firms explore data — moving beyond text-to-SQL into scenario modelling, strategy development and real partnership with the LLM. Here's why ClickHouse is the right database to build on.

Comparing the four ways to deploy ClickHouse - Open Source, Cloud, Bring Your Own Cloud, and Private - and the strengths and weaknesses of each.

How to implement OHLC candlestick charts in ClickHouse using AggregatingMergeTree and materialized views for real-time, multi-resolution financial data at any time granularity.

How ClickHouse have assembled ClickHouse, the ClickHouse MCP server and LibreChat into an integrated agentic data stack — and why this combination is well suited for building agentic analytics and internal agent teams.

Introducing Query Dog, an open source tool for analysing and optimising ClickHouse query performance through visualisation of the query log and system tables.

Learn why ClickHouse partitions can hurt rather than help performance, and what alternatives to consider.

After building 3 data lakehouses, I'm skeptical about this architecture. Here's what I learned from these projects.

Exploring the TCO when deciding between ClickHouse Cloud vs Open Source.

A bake off of Claude and Gemini in order to test their SQL performance against a ClickHouse database.

A demonstration of a real time sports analytics use case powered by ClickHouse.

How ClickHouse can simplify your data estate and technology stack.

Using ClickHouse String and Array Processing functions to analyse FIX Protocol data.

A video walkthrough of PeerDB open source and ClickPipes for Postgres CDC.

Using ClickPipes to reliably replicate data from PostgreSQL to ClickHouse.

Using PeerDB open source to reliably replicate data from PostgreSQL to ClickHouse.

An example of how I classify time series by leveraging machine learning functions baked into ClickHouse.

Operational Analytics is about using real time data to make better day to day decisions

In recent blog posts I demonstrated how data science techniques such as forecasting and anomaly detection can be implemented entirely within ClickHouse using only SQL.

Using functions integrated into ClickHouse to implement a linear regression.

I’ve recently used K6 to directly load test a ClickHouse environment and found it to be very powerful and expressive.

In this article I analyse the differences in pricing between Snowflake and ClickHouse Cloud

This article is part of a series where I look at doing data science work within ClickHouse

Last week I delivered a webinar with the Cube team, covering why I believe that Cube is an excellent fit for building data-oriented applications.

Machine learning professionals can learn how to combine ClickHouse Cloud and AWS SageMaker for powerful data analytics and predictive modeling. This comprehensive guide demonstrates end-to-end workflow including data preparation, feature engineering, model training, deployment, and real-time inference, with practical insights into traffic volume forecasting using CatBoost algorithm.

ClickHouse is increasingly being used by capital markets firms such as investment banks, hedge funds and trading technology vendors.

The cost model behind many cloud data warehouses is not defined for low latency big data workloads.

This article walks through a workforce allocation problem and demonstrates how to solve it using ClickHouse Cloud and Google OR-Tools.

When you think of data warehouse technology, you may think of legacy on premise offerings such as Oracle or Terradata, or more modern cloud based platforms such as Snowflake, AWS Redshift or BigQuery.

A feature store is a place to store the cleaned and prepared input data used to train a machine learning model

How ClickHouse cloud has the potential to take complexity out of your data stack.

In recent years, when I have been asked by businesses which data warehouse to choose, there were 3 candidates at the top of the list

Learn how large language models can be used to interrogate a real time analytical database like ClickHouse Cloud.

Two of my favourite tools which combine to be a brilliant serverless stack for data and analytics.

This article is part of a series where I look at doing data science work within ClickHouse

A walkthrough of my architecture for a DeFi analytics platform.

Learn how to effectively combine dbt and ClickHouse for data transformation and analytics. Discover best practices, use cases, and implementation strategies for leveraging dbt's transformation capabilities with ClickHouse's high-performance database.

ClickHouse is a uniquely differentiated database which I decided to take a big bet on.

Learn how to control memory usage when performing GROUP BY queries on large datasets in ClickHouse.