Stream data from Azure SQL Server to ClickHouse Kafka API
Move data from Azure SQL Server to ClickHouse Kafka API in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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How to integrate Azure SQL Server with ClickHouse Kafka API in 3 simple steps
Connect Azure SQL Server as your data source
Set up a source connector for Azure SQL Server in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
Configure ClickHouse Kafka API as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to ClickHouse Kafka API.
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.

Azure SQL Server connector details
The Microsoft SQL Server connector captures real-time change data (CDC) from your SQL Server database and streams it into Estuary collections, ensuring continuous synchronization across systems.
- Continuous CDC ingestion: Streams inserts, updates, and deletes directly from SQL Server’s CDC change tables into Estuary collections for real-time data pipelines.
- Broad platform support: Works seamlessly across self-hosted, Azure SQL Database, Amazon RDS, and Google Cloud SQL instances.
- Automatic schema and instance handling: Detects new capture instances when DDL changes occur (like added columns) and automatically transitions to updated change tables.
- Granular permissions and automation: Supports automatic capture instance management and change table cleanup when granted
db_ownerprivileges. - Flexible and secure connectivity: Connect via SSH tunneling or IP allowlisting for on-premise and cloud-hosted deployments.
- Customizable configuration: Includes advanced tuning options for backfill chunk size, table skipping, and metadata tagging.
💡 Tip: For optimal reliability, enable Automatic Capture Instance Management to let Estuary handle table-level CDC setup and schema evolution automatically, while ensuring the user has the necessary db_owner privileges.

ClickHouse Kafka API connector details
The ClickHouse Kafka API connector uses Estuary’s Dekaf compatibility layer to expose Estuary collections as Kafka-compatible topics that ClickHouse Cloud can ingest through ClickPipes. Instead of managing your own Kafka broker, you create a Dekaf materialization in Estuary and configure ClickPipes to read from Estuary using Kafka-compatible connection details. This is useful when your ClickHouse workflow depends on ClickPipes or when you want ClickHouse Cloud to manage the ingestion path.
- Materialize Estuary collections as Kafka-compatible topics that ClickHouse ClickPipes can consume.
- Use Dekaf when you want to ingest into ClickHouse Cloud through ClickPipes instead of writing directly with Estuary’s native ClickHouse connector.
- Connect ClickPipes using Estuary’s Dekaf broker address, schema registry address, SASL_SSL, and the PLAIN SASL mechanism.
- Use the full Dekaf materialization name as the Kafka and schema registry username.
- Use the Dekaf auth token as the Kafka and schema registry password.
- Map each Estuary collection binding to a Kafka-compatible topic for ClickHouse ingestion.
- Stream CDC updates from source systems into ClickHouse with low-latency, incremental delivery.
- Configure deletion behavior based on how you want ClickHouse to handle CDC-aware deletes and Kafka-style delete events.
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.

Azure SQL Server to ClickHouse Kafka API pricing estimate
Estimated monthly cost to move 800 GB from Azure SQL Server to ClickHouse Kafka API is approximately $1,000.
Data moved
Choose how much data you want to move from Azure SQL Server to ClickHouse Kafka API each month.
GB
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Why pay more?
Move the same data for a fraction of the cost.



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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this Azure SQL Server to ClickHouse Kafka API integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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