---
title: ClickHouse Data Warehouse
sidebarTitle: ClickHouse Data Warehouse
logo: /images/integrations/clickhouse_icon.svg
description: Export Langfuse data as Parquet and load it into ClickHouse Cloud with an S3 ClickPipe.
---

# ClickHouse Data Warehouse

  **Work in progress:** This is the recommended path. A detailed setup guide
  will follow. Questions or requirements? [Contact support](/support) or [open a
  GitHub issue](https://github.com/langfuse/langfuse-docs/issues/new).

Export Langfuse data to ClickHouse to analyze AI activity alongside business and product data — for example, join usage and cost with customer accounts, or relate agent quality to product outcomes.

## Recommended integration: Parquet export and an S3 ClickPipe [#recommended-integration]

Write Parquet files to Amazon S3 with Langfuse's blob storage export, then load them into ClickHouse Cloud with an S3 ClickPipe.

```mermaid
flowchart LR
    langfuse["Langfuse"] -->|"Scheduled blob storage export"| s3["Amazon S3 / Parquet files"]
    s3 --> pipe["S3 ClickPipe"]
    pipe --> ch["ClickHouse Cloud"]
```

1. **Export from Langfuse.** In **Settings → Integrations → Blob Storage**, configure an S3 destination and choose Parquet. See [Export to blob storage](/docs/api-and-data-platform/features/export-to-blob-storage) for setup and availability.
2. **Create an S3 ClickPipe.** Point ClickHouse Cloud at the exported files and enable continuous ingestion. See the [S3 ClickPipe setup guide](https://clickhouse.com/docs/integrations/clickpipes/object-storage/amazon-s3/get-started).
3. **Analyze in ClickHouse.** Use the [export field reference](/docs/api-and-data-platform/features/blob-storage-export-fields) when joining Langfuse data with your other datasets.

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/integrations/clickhouse/data-warehouse.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx @langfuse/cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.