How ClickHouse Uses Python

This article details how ClickPy, a free Python download statistics platform powered by ClickHouse, scaled to over 2 trillion rows by replacing its legacy cron-based ingestion pipeline with ClickPi...

The article discusses the development of chDB, a Python library that integrates ClickHouse with Pandas DataFrames for high-performance SQL querying.

At ClickHouse, we don't like the word "impossible." We believe that with the right tools, everything is a data problem. To prove it, we decided to complete the 2025 Advent of Code unconventionally: using pure ClickHouse SQL.

The November 2025 edition of What's New in ClickStack highlights several new features and improvements in the open-source observability stack built for ClickHouse.

This article benchmarks five major cloud data warehouses—Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift—across various scales of data to compare their cost-performance.

The article details the journey of upgrading the chDB kernel from ClickHouse v25. 5 to v25. 8. 2.

The October 2025 edition of What's New in ClickStack highlights significant updates to the open-source observability stack for ClickHouse, including the introduction of alerting features, customiza...

This article discusses how to enhance log compression through log clustering techniques in ClickHouse, focusing on transforming unstructured logs into structured data for efficient storage.

This article explores the process of tracing OpenAI agents using ClickStack, demonstrating how to build an OpenAI agent that interacts with ClickHouse and visualizes the decision-making process.

This article discusses how to instrument a Next. js application using OpenTelemetry and ClickStack, focusing on the integration of observability and analytics through ClickHouse.

This article discusses the process of creating reproducible ZIP archives for AWS Lambda functions, focusing on challenges such as file order, timestamp management, and OS compatibility.

This article discusses the implementation of Change Data Capture (CDC) from Delta Lake to ClickHouse, detailing the architecture, components, and a reference implementation in Python.

The article explores the potential need for Object-Relational Mappers (ORMs) in Online Analytical Processing (OLAP) environments, particularly focusing on ClickHouse.

The article discusses the importance of LLM observability using ClickStack, OpenTelemetry, and MCP, highlighting how to instrument LibreChat for enhanced insights into AI-driven applications.

The article explores the analysis of Wimbledon tennis data using ClickHouse, detailing the unique scoring system of tennis and how to implement a function to compute points needed to win a game.

The article discusses the integration of ClickHouse with the Model Context Protocol (MCP), highlighting its benefits for connecting third-party services to large language models (LLMs).

The article discusses how ClickHouse efficiently queries Parquet files, a key storage format for Lakehouse architectures, without requiring data ingestion.

The article introduces AgentHouse, an interactive demo environment that integrates ClickHouse's real-time analytics with Anthropic's large language model, Claude Sonnet.

The article announces the launch of Ruby gem analytics powered by ClickHouse, enabling Ruby developers to analyze gem download data since 2017.

This article provides a comprehensive guide on integrating PostgreSQL with ClickHouse using Change Data Capture (CDC) for real-time analytics.

This article explores the various input formats supported by ClickHouse for data ingestion, focusing on performance and efficiency.

This article discusses the implementation of the Medallion architecture using ClickHouse, a powerful database management system.

This article discusses building single page applications (SPAs) using ClickHouse with a focus on a 'client only' architecture.

This article explores the integration of the Perspective library with ClickHouse to create real-time visualizations of streaming Forex data.

This article discusses how to quickly integrate analytics into applications using ClickHouse Cloud Query Endpoints, highlighting the benefits of API endpoints for simplifying SQL interactions.

This article discusses how to model machine learning data in OLAP databases, specifically using ClickHouse as an example.

CryptoHouse is a free blockchain analytics platform powered by ClickHouse, enabling real-time SQL queries on blockchain data.

ClickHouse Release 24. 6 introduces 23 new features, 24 performance optimizations, and 59 bug fixes, enhancing its capabilities for data management and analysis.

This article compares ClickHouse and Elasticsearch in terms of performance for large-scale data analytics, particularly focusing on `count(*)` aggregations over billions of rows.

This article compares ClickHouse and Elasticsearch, focusing on their mechanics for count aggregations.

This article discusses how to implement K-Means clustering using ClickHouse SQL, demonstrating its efficiency in handling large datasets, such as 170 million NYC taxi rides, in under 3 minutes.

This article explores how ClickHouse can be utilized as a feature store to train machine learning models, specifically focusing on the integration with Featureform.

This article discusses the management of ClickHouse schemas as code using the Atlas tool, highlighting the transition from schema-less technologies to structured data management.

ClickHouse Release 24. 2 introduces significant enhancements including 18 new features, 18 performance optimizations, and 49 bug fixes.

This article discusses ClickHouse's performance in handling large datasets, specifically addressing the 1 trillion row challenge.

This article details the process of building a chatbot named 'HackBot' that utilizes data from Hacker News and Stack Overflow, leveraging ClickHouse and LlamaIndex.

This article explores the use of Apache Iceberg and ClickHouse to analyze global internet speeds using the Ookla dataset.

The article discusses a hybrid query execution experiment using ClickHouse, highlighting the use of ClickHouse Local and ClickPy to analyze GitHub metrics alongside PyPi package downloads.

This article explores the role of Feature Stores in MLOps and how ClickHouse can enhance their performance and flexibility.

This article explores the implementation of linear regression using ClickHouse's machine learning functions, focusing on predicting delivery times based on distance and pickup hour.

This article explores the implementation of Approximate Nearest Neighbor (ANN) vector search using SQL-powered Local Sensitive Hashing (LSH) and random projections in ClickHouse.

This article discusses the development of a Retrieval-Augmented Generation (RAG) pipeline for Google Analytics using ClickHouse and Amazon Bedrock.

The article discusses how to query Pandas DataFrames using ClickHouse through the chDB library, enabling users to leverage ClickHouse's SQL capabilities for data analysis.

ClickHouse Release 23. 10 introduces a range of new features, performance optimizations, and bug fixes, enhancing its capabilities for data processing and analytics.

This article discusses how to leverage ClickHouse's machine learning functions for forecasting, specifically using Stochastic Linear Regression and evalMLMethod.

ClickHouse Release 23. 9 introduces a variety of new features, performance optimizations, and bug fixes aimed at enhancing user experience and functionality.

ClickHouse Keeper is an open-source alternative to ZooKeeper, designed for better resource efficiency and performance in distributed systems.

The article discusses chDB, a Python module that embeds the ClickHouse OLAP engine, enabling efficient SQL execution on large datasets.

This article discusses optimizing large data loads in ClickHouse by focusing on performance and resource usage factors.

ClickHouse Release 23. 8 introduces a variety of new features, performance optimizations, and bug fixes, enhancing the capabilities of this columnar database.