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10 Best ClickHouse ETL Tools for 2026

Compare the 10 best ClickHouse ETL tools for 2026 on connectors, CDC support, deployment and cost, with pros and cons for each.

Team Airbyte

March 31, 2026

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TL;DR

This guide compares 10 ClickHouse ETL tools for moving data in and out of ClickHouse.

Connector-led ELT: Airbyte, Fivetran, Stitch Data and Hevo Data handle the sync with prebuilt connectors rather than custom scripts.
Transformation-heavy: Matillion, Talend and Pentaho suit pipelines where reshaping the data matters as much as moving it.
Orchestration and open source: Airflow schedules and monitors, Meltano is CLI-first, and Rivery packages orchestration into a managed product.
Three common use cases: warehouse synchronisation, BI tool integration and feeding downstream machine learning pipelines.

ClickHouse ETL tools move data between ClickHouse and the rest of your stack, which is where teams usually hit friction once the database itself is humming along on heavy analytical workloads. The typical jobs are syncing to a data warehouse, feeding BI dashboards, and supplying downstream ML pipelines. The ten tools below cover those patterns, along with how to choose between them.

ClickHouse is built for fast ingestion and fast analytical queries, but it rarely sits alone. Most teams need that data somewhere else as well, whether that is a warehouse for broader analysis, a BI tool that cannot query ClickHouse directly, or a feature store feeding models.

This guide covers the ETL tools that work well with ClickHouse, what each is built for, and the criteria worth applying before you commit to one.

What Is ClickHouse ETL?

ClickHouse ETL is the process of extracting analytical data from ClickHouse and loading it into other systems or databases using ETL tools. In practice that means building a pipeline that moves ClickHouse data wherever it is needed, whether that is a data warehouse, a BI tool, or any other system that has to read it.

How Clickhouse ETL Tools Work

What Are the Common Use Cases for ClickHouse ETL?

Data Warehouse Synchronization

Moving your high-volume analytics data from Clickhouse to data warehouses like Snowflake or BigQuery is a common requirement. This lets you combine Clickhouse's fast ingestion capabilities with your warehouse's broader analytics ecosystem, giving you the best of both worlds.

BI Tool Integration

Your business analysts need fresh data in their BI tools like Tableau or Looker, but these tools might not directly connect to Clickhouse. ETL tools bridge this gap by regularly syncing Clickhouse data to supported databases that your BI tools can easily query.

Machine Learning Pipeline Support

Data scientists often need Clickhouse data in their ML training environments or feature stores. Clickhouse ETL tools can automatically transform and load your Clickhouse data into formats and destinations that ML frameworks can readily consume, keeping your models updated with fresh training data.

Which Are the Best ClickHouse ETL Tools?

Tool	Type	Connectors	CDC Support	Deployment	Pricing Model
Airbyte	Open-source ELT	600+	Yes	Cloud, hybrid (Flex), self-managed	Free OSS; capacity-based paid tiers
Fivetran	Managed ELT	700+	Yes	Cloud	Monthly active rows
Stitch Data	Managed ELT	140+	Limited	Cloud	Row volume tiers
Matillion	Self-hosted ELT	100+	Limited	Self-hosted, cloud	Credit-based
Apache Airflow	Orchestration	Operators, not connectors	You build it	Self-managed	Free, infrastructure cost only
Talend	Enterprise ETL	1,000+	Yes	On-premises, cloud	Subscription, custom
Pentaho	Enterprise ETL	Broad	Limited	On-premises, cloud	Enterprise licence
Rivery	Managed ELT	150+	Yes	Cloud	Usage-based credits
Hevo Data	Managed ELT	150+	Yes	Cloud	Event-based tiers
Meltano	Open-source ELT	Singer taps and targets	Via taps	Self-managed	Free, open source

Here are the top ClickHouse ETL tools based on their popularity and the criteria listed above:

1. Airbyte

Airbyte is an open data movement platform that extracts data from ClickHouse and loads it into your chosen destinations using pre-built connectors. With 600+ connectors, it automates the pipeline while leaving you full control, including the ability to edit connectors or build new ones when an endpoint is missing.

Deployment Options: Both cloud and self-hosted

Platform Type: Open-source with managed cloud option

Key Features:

Pre-built Clickhouse connector with support for incremental sync
Visual interface for pipeline configuration and monitoring
CDC support and flexible data transformation options

Hybrid deployment: with Enterprise Flex, Airbyte runs the control plane while the data plane runs inside your own cloud, VPC or data centre. Records, credentials and encryption keys never leave your boundary, connections are outbound only, and you can bring your own KMS. Region pinning and air-gapped deployment are both supported.

Pros	Cons
600+ connectors, the widest coverage in this comparison	Transformation relies on dbt rather than being built in
Incremental sync and CDC keep destinations current	Self-managed deployment needs Kubernetes or Docker skills
Connectors are open source and editable	Not an orchestrator, so pair it with Airflow or Dagster
Enterprise Flex keeps data and keys inside your own VPC	Flex pricing is custom and needs a sales conversation
Capacity-based pricing keeps costs flat as volume grows	Advanced governance sits on the paid tiers

2. Fivetran

Fivetran offers a reliable, fully managed solution for extracting data from Clickhouse and loading it into popular destinations. It handles schema changes and pipeline maintenance without requiring engineering resources.

Deployment Options: Cloud-only

Platform Type: Proprietary SaaS

Key Features:

Modern pipeline architecture
Automated schema change management
Built-in data quality monitoring and alerts

Here are more critical insights on the key differentiations between Airbyte and Fivetran

Pros	Cons
Fully managed, with schema changes handled for you	Monthly active row pricing is hard to forecast
Built-in data quality monitoring and alerts	Closed source, so connectors cannot be edited
No engineering resource needed for upkeep	Cloud only, so data is processed on vendor infrastructure
Large connector catalogue	Among the more expensive options at volume

3. Stitch Data

Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.

Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.

What's unique about Stitch?

Stitch competes primarily on price rather than breadth, which suits teams with a small number of common sources and modest volumes. The trade-off is a narrower connector catalogue and less depth in transformation than the larger platforms offer.

Here are more insights on the differences between Airbyte and Stitch.

Pros	Cons
Among the lowest-cost managed options	Narrower connector catalogue than the leaders
Built on the open Singer specification	Only the most used Singer taps are actively maintained
Simple setup with a short time to first sync	Limited transformation capability
Transparent row-volume pricing	Ownership has changed hands several times

4. Matillion

Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

What's unique about Matillion?

Being self-hosted means Matillion keeps your data inside your own infrastructure. The trade-offs are that multi-cloud setups may require several paid instances, and that Matillion has verticalised its offering to cover the whole ELT process, so integration with tools like dbt and Airflow is more limited than with composable platforms.

Here are more insights on the differentiations between Airbyte and Matillion.

Pros	Cons
Self-hosted, so data stays in your infrastructure	Around 100 connectors, fewer than the leaders
Covers extract, load and transform in one tool	Multi-cloud setups may need several paid instances
Strong visual transformation designer	Limited integration with dbt and Airflow
Warehouse-native processing keeps performance high	Credit-based pricing needs active monitoring

5. Airflow

Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

What's unique about Airflow?

Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

Here are more insights on the differentiations between Airbyte and Airflow.

Pros	Cons
Free, open source and very widely adopted	Not an ETL tool, so it ships no ClickHouse connector
Pipelines defined in Python and version controlled	You build and maintain extraction logic yourself
Large operator library across major cloud platforms	Production operation takes real engineering effort
Pairs well with a connector-based tool for movement	Steep learning curve for teams new to DAGs

6. Talend

Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

What’s unique with Talend?

What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.

Pros	Cons
Integration, quality and governance in one platform	Implementation is involved and rarely self-serve
Open Studio allows customisation and extension	Enterprise pricing puts it beyond smaller teams
Very broad connector catalogue	Steep learning curve and long onboarding
Strong data quality and governance features	Roadmap now tied to Qlik after acquisition

7. Pentaho

Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

What is unique about Pentaho?

What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.

However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

Pros	Cons
Combines ETL with analytics, reporting and data mining	ETL only, so it misses the benefits of the ELT model
Open-source roots allow customisation	Enterprise product with no self-serve entry point
Machine learning and predictive analytics built in	Interface feels dated against modern platforms
Broad source and destination coverage	Heavier than most ClickHouse pipelines require

8. Rivery

Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.

Pros	Cons
Bundles ingestion, transformation and orchestration	150+ connectors, fewer than the leaders
Data activation pushes results back to business tools	Credit pricing varies by connector, making cost hard to model
One platform rather than several to integrate	Cloud only, with no self-hosted option
Reasonable learning curve for analytics teams	Less flexible than composable open-source stacks

9. HevoData

HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.

Pros	Cons
Built-in transformation, mapping and enrichment before load	Around 150 integrations, well short of the leaders
Data activation syncs results back to source APIs	Event-based pricing is hard to forecast at scale
Short setup time with a no-code interface	Cloud only, so no self-hosted or hybrid option
Automatic schema mapping on incoming data	Connector gaps cannot be filled by your own team

10. Meltano

Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.

Pros	Cons
Free, open source and Git-native	CLI-first, so no interface for non-technical users
Strong DataOps and CI/CD orientation	Connector maintenance is left to the Singer community
SDK available for building connectors	No support package with an SLA
Works well alongside dbt and Airflow	Building connectors takes more effort than Airbyte's CDK

All those ETL tools are not specific to ClickHouse, you might also find some other specific data loader for ClickHouse data. But you will most likely not want to be loading data from only ClickHouse in your data stores.

How Do You Choose the Right ClickHouse ETL Tool?

As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your ClickHouse ETL solution.

Here is our recommendation for the criteria to consider:

Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
Ability to build new connectors: all data integration solutions support a limited number of data sources.
Support of change data capture: this is especially important for your databases.
Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

How Do You Start Pulling Data From ClickHouse?

If you decide to test Airbyte, you can start moving your ClickHouse data within minutes in three steps:

Step 1: Set up ClickHouse as a source connector

1. Navigate to the Airbyte dashboard and click the "Sources" tab.
2. Click "Add Source" and select "ClickHouse" from the list of available sources.
3. Enter the connection details for your ClickHouse database, including host, port, database name, username and password.
4. Configure SSL or SSH tunnelling if your deployment requires it.
5. Select the tables and columns you want to replicate.
6. Click "Test Connection" to confirm Airbyte can reach the database.
7. Once the test passes, save the source and it is ready to use in a connection.

Step 2: Set up a destination for your extracted ClickHouse data

Choose the destination you want your ClickHouse data to land in. This can be a cloud data warehouse, data lake, database, cloud storage service or any other supported Airbyte destination.

Step 3: Configure the ClickHouse data pipeline in Airbyte

Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

The process is the same whether you deploy Airbyte Open Source in a few minutes or start with Airbyte Cloud.

Which ClickHouse ETL Tool Should You Choose?

The ten tools above cover the realistic options for moving data out of ClickHouse. If connector breadth and deployment flexibility matter most, Airbyte fits. If you want pipelines someone else maintains, Fivetran, Stitch or Hevo. If transformation is the bulk of the work, Matillion, Talend or Pentaho. And if you already run an orchestrator, Airflow or Meltano keep everything defined in code.

Suggested Reads:

Open Source Database Software

Data Pipeline Tools

Change Data Capture Tools

Big Data Analytics Tools

About the Author

Team Airbyte

Engineering Team of Airbyte.

10 Best ClickHouse ETL Tools for 2026

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