How ClickHouse Uses Pandas

You can choose any of these hundred database systems and run queries. You can create tables and databases, insert data, drop tables, etc. Every database comes with a preloaded dataset of 100 million records, so you can test example queries. It has not onl

A Solutions Architect’s experience building a retail analytics platform with AI agents, real-time dashboards, and full observability on the ClickHouse data platform

chDB embeds a full ClickHouse query engine inside an agent's own process, turning data access, memory, and federation into local function calls instead of network round trips, cutting the latency, retries, and token waste that come with remote queries.

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

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

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

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.

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

The article discusses ClickHouse's tool, clickhouse-local, which is designed for fast querying of large JSON files.