Stream into ClickHouse with your free account
Deliver data from databases, SaaS apps, APIs, files, and streaming systems into ClickHouse using Estuary's pre-built connectors. Run pipelines from real-time streaming to scheduled batch based on connector capabilities.
- <100ms to batch
- 200+ connectors
- Managed data pipelines
ClickHouse connector details
The ClickHouse materialization connector in Estuary delivers data from Estuary collections directly into ClickHouse tables using ClickHouse’s Native protocol and Native format. It works with both self-hosted ClickHouse and ClickHouse Cloud and supports continuous delivery from real-time or batch data pipelines.
- Native ClickHouse integration using the Native protocol and Native format
- Automatic table creation for each configured materialization binding
- Standard updates using
**ReplacingMergeTree**to keep the latest version of each keyed record - Optional delta updates using
MergeTreefor append-oriented workloads - Soft deletes by default, tracked through the
_meta/opfield - Optional hard deletes using ClickHouse tombstones
- Configurable sync schedules when periodic delivery is preferred
- TLS/SSL connection support, with full certificate verification enabled by default
- Supports both self-hosted ClickHouse and ClickHouse Cloud
In standard update mode, Estuary writes new record versions to a ReplacingMergeTree table and uses flow_published_at as the version column. ClickHouse performs deduplication during background merges, and queries can use FINAL when they need the latest deduplicated view immediately.
How to connect your data source to ClickHouse in 3 easy steps
- 1
Connect your data source
Select from more than 100 supported databases and SaaS platforms including PostgreSQL, MySQL, SQL Server, MongoDB, and Kafka.
- 2
Prepare and transform your data
Apply transformations and schema mapping as data moves whether you are streaming in real time or loading in batches.
- 3
Sync to ClickHouse
Continuously or periodically deliver data into your destination with support for change data capture and reliable delivery for accurate insights.
Learn more with some related videos
Dive deeper into ClickHouse with tutorials and walkthroughs from our YouTube channel.
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Estuary 101: Right Time Data Integration for ClickHouse
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PostgreSQL CDC: What, Why, and How
Set up PostgreSQL change data capture (CDC) without worrying about your WAL or replication slot loss. This step-by-step Postgres CDC guide covers WAL configuration, replication user setup, publication creation, and building your first real-time pipeline to Snowflake, Iceberg, or ClickHouse, no Kafka or Debezium required.
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Oracle CDC Setup Tutorial: Stream Oracle Data in Real Time with Estuary
Learn how to set up **Oracle Change Data Capture (CDC)** with Estuary and continuously capture changes from an Oracle Database using **Oracle LogMiner**.
Trusted by data teams worldwide
All data connections are fully encrypted in transit and at rest. Estuary also supports private cloud and BYOC deployments for maximum security and compliance.
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HIGH THROUGHPUT
Distributed, event-driven architecture scales for demanding data workloads.
DURABLE COLLECTIONS
Store data as it moves so you can transform, replay, and deliver it downstream.
FLEXIBLE LATENCY
From <100ms CDC and real-time streaming to scheduled batch, depending on connector capabilities.
From <100ms to batch
Estuary supports CDC, real-time streaming, incremental syncs, and scheduled batch across its connector ecosystem. Bring data from databases, SaaS apps, APIs, files, and event streams into ClickHouse, with capture and delivery cadence based on connector capabilities and pipeline configuration.
- Connect data from operational databases, SaaS applications, APIs, files, and streaming systems to ClickHouse through Estuary.
- Transform and route data through Estuary collections before delivering it to ClickHouse.
See what you can connect to ClickHouse:
Don't see a connector?Request and our team will get back to you in 24 hours
Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | Kafka | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Latency | <100ms to scheduled | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale | ||||
| Maintenance Effort | Low | Medium | High | High |
One platform for real-time and batch data pipelines
Deliver real-time and batch data from DBs, SaaS, APIs, and more
