ClickHouse Integration: A Practical Guide for Data Teams in 2026

We’ve shipped ClickHouse Cloud as a destination on Artie. You can now stream changes from Postgres, MySQL, SQL Server, Oracle, and MongoDB into ClickHouse Cloud in less than 10 minutes, with sub-minute latency, automatic schema evolution, and parallel backfill.

We’re also excited to announce that Artie is in the inaugural cohort of House Mates, ClickHouse's partner community and program. House Mates is built around joint go to market, technical collaboration, and a catalog of curated integrations that customers can count on.

Why we built it: the gap between "the warehouse my team uses for monthly reporting" and "the analytical store powering my live operator dashboard" has gotten too wide to bridge with batch ETL. Picture 7pm on a Friday in San Francisco. Your food-delivery dashboard still shows this morning's average delivery times, while one zone has gone from 22-minute average prep to 41 minutes because three popular restaurants got slammed at once. By the time the nightly batch job loads tomorrow's data, the surge will be over and the 1-star reviews will already be in.

A batch data warehouse handles the monthly business review, but it’s not the right tool for that live operator console. More teams are standing up ClickHouse Cloud as a real-time serving layer alongside it. The rest of this post is the practical version: what a ClickHouse integration actually is, why teams are reaching for it now, and how to wire your source database into ClickHouse Cloud using the integration we just shipped.

Artie is a fully managed real-time streaming platform that continuously replicates data into warehouses and lakes. We automate the entire data ingestion lifecycle, from capturing changes to merges, schema evolution, backfills, observability, and scales to billions of change events per day.

This guide is written for data engineers and engineering leads who already run Postgres or MySQL and want to add ClickHouse Cloud as a real-time analytical layer alongside their operational database.

Key Takeaways

• Use ClickHouse for sub-second analytical queries on large datasets, as a real-time layer alongside your transactional database, or as your primary analytical store.  It's a columnar OLAP store built for sub-second analytical queries on huge datasets, and it pairs with your transactional and warehouse layers rather than substituting for either.
• Pick a MergeTree-family table engine and batch your writes. Single-row inserts are the most common way teams shoot themselves in the foot on ClickHouse; CDC tools should batch every few seconds before writing.
• Choose log-based CDC over batch ETL whenever your freshness target is under 15 minutes. Reading from the Postgres WAL or MySQL binlog adds seconds of latency, not the 15-60 minutes batch jobs cost you.
• Use a managed pipeline if you don't want to operate Kafka. Artie streams Postgres, MySQL, SQL Server, Oracle, and MongoDB into ClickHouse Cloud with sub-minute latency, runs backfills in parallel with the live stream, and handles schema changes automatically.

What Is a ClickHouse Integration?

Concretely, integrating ClickHouse means continuously moving rows from your Postgres orders, restaurants, and couriers tables (or whatever your equivalent set of high-traffic tables is) into ClickHouse Cloud within seconds of those rows changing in production. A clickhouse integration is the set of choices about table engine, batching strategy, and replication transport that turns a stream of source-database changes into queryable analytics rows.

At a high level, the path looks like this:

‍ClickHouse is a different kind of destination. It’s purpose-built for analytical scans at scale, with columnar storage and vectorized execution that delivers sub-second queries on billions of rows.

ClickHouse uses MergeTree-family engines. MergeTree is the on-disk format ClickHouse uses to organize data for fast columnar scans. Updates aren't applied in place. New rows get appended and the engine merges them asynchronously. For CDC you'll use ReplacingMergeTree, which deduplicates rows on primary key during merge.

Ingestion is batched. Single-row inserts are an anti-pattern in ClickHouse. They create too many small parts on disk and starve the merge process. Streaming CDC tools batch changes every few seconds before writing.

Why Data Teams Are Moving to ClickHouse

An operator console like the one above needs to scan every order from the last five minutes, grouped by delivery zone, in under a second. Snowflake can answer the query, but at the cost of a constantly-warm warehouse for one dashboard, and p99 still creeps past two seconds at peak. ClickHouse turns the same query into a sub-100ms scan on commodity hardware. Once you've watched a single ClickHouse node out-scan an L-sized Snowflake warehouse on five years of order data, it's hard to go back to running both dashboards out of the same place.

Columnar storage and vectorized execution. A query that touches three columns reads three tight blocks off disk instead of every row in the table. Combined with vectorized execution (processing values in batches inside CPU registers), this is how ClickHouse gets sub-second p99 on datasets that would otherwise need an L-sized Snowflake warehouse and aggressive clustering.

Real-time use cases that warehouses struggle with. Product analytics (PostHog runs on ClickHouse), observability (Cloudflare, Uber), user-facing dashboards, AI feature stores, and LLM trace logs. The shared pattern is high-cardinality event data with sub-second query SLAs that don't pencil out economically on a warehouse.

ClickHouse Cloud removed the operational barrier. Running ClickHouse used to mean operating your own clusters with manual shard balancing and replication. ClickHouse Cloud handles that, which is most of why adoption has accelerated over the last 18 months.

Worth knowing: ClickHouse's append-and-merge-later design isn't an oversight, it's a deliberate trade. Most databases optimize for low-latency updates and pay for it on read. ClickHouse went the other direction, giving up immediate update semantics so it can ingest millions of rows per second on a single node and still scan billions of rows at query time.

It's fair about the tradeoffs too. ClickHouse isn't a transactional store, so keep your OLTP database for that. Joins are workable but not its strength, so denormalize where you can. And ReplacingMergeTree is eventually consistent on dedup unless you query with FINAL (which costs) or maintain projections.

How to Integrate ClickHouse with Artie

Wiring an existing Postgres source into a fresh ClickHouse Cloud service takes about 10 minutes on a working laptop.

Prep your Postgres source. Set wal_level = logical on the source database. In RDS this is a parameter group change plus a reboot; on self-hosted Postgres, edit postgresql.conf and restart. The WAL is Postgres's write-ahead log, the same log it already writes for crash recovery, and logical replication lets external consumers like Artie read it as a change stream. Then create a publication and Artie's service account:

CREATE PUBLICATION dbz_publication FOR ALL TABLES;

CREATE ROLE artie WITH LOGIN PASSWORD '<password>' REPLICATION;
GRANT USAGE ON SCHEMA public TO artie;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO artie;

If a table you want to replicate (say, orders) doesn't have a primary key, fix that before connecting. CDC needs one to deduplicate. See Artie's primary key guide for the safe way to add one to a live table.

Stand up ClickHouse Cloud. Create a service in ClickHouse Cloud, then in the "Connect with" panel select "Go" and copy the warehouse address (Artie's connector uses the Go protocol). Create Artie's user, copied from Artie's ClickHouse destination docs:

CREATE USER artie IDENTIFIED BY '<password>';
GRANT CURRENT GRANTS ON *.* TO artie;
REVOKE ALL ON default.* FROM artie;

Create the pipeline in Artie. In the Artie dashboard, click + New pipeline and pick Postgres as the source. Paste your Postgres host and credentials, then choose a connection method: direct IP allowlist, SSH tunnel, or AWS PrivateLink. Select the tables to replicate (for a food-delivery setup, that's typically orders, restaurants, and couriers). Artie acts as the data integration tool between your Postgres and ClickHouse Cloud, capturing every change, evolving the schema as app developers add columns, and merging into ClickHouse's MergeTree tables without you writing the merge logic. Then pick ClickHouse as the destination and paste the warehouse address.

Configure table-level settings. Three knobs worth setting:

• Soft deletes set __artie_deleted = true instead of dropping the row. Usually the right call for analytics.
• __artie_updated_at gives downstream incremental models a watermark column.
• History mode keeps SCD2-style row history on tables where every change matters, useful for a restaurants table where rating changes need to be auditable.

Start the pipeline. Backfill and live CDC run in parallel. CDC events queue while the backfill snapshots existing rows, then apply in order with no duplication or gaps (Artie's backfill mechanics). First rows land in ClickHouse within seconds. The first time you watch a 50M-row table backfill in 90 seconds while CDC events stream cleanly through behind it, the architecture clicks. From the Analytics Portal you can watch lag and rows synced, and set up Slack alerts on schema changes.

If you'd rather not run Debezium, a Kafka cluster, and a custom ClickHouse sink connector yourself, Artie handles the pipeline as a managed service. We walked through the broader CDC landscape in our writeup of the best CDC tools for cloud data warehouses. The short version: Debezium and Airbyte give you control but you operate Kafka; Fivetran is easy but degrades to batch under load; cloud-native options like AWS DMS and Google Datastream lock you to one cloud and tend to break under sustained CDC. With the integration we just shipped, getting from "Postgres in production" to "fresh rows in ClickHouse Cloud" is about a 10-minute job.

FAQ

What databases can be integrated with ClickHouse?

Any OLTP source with a working CDC transport. The common ones are Postgres (via logical replication), MySQL (binlog), and SQL Server (change tracking or CDC). Document stores like MongoDB and DynamoDB work via change streams. Artie supports all of those plus Oracle, CockroachDB, and Cassandra-family stores.

Is CDC the best method for ClickHouse integration?

For real-time workloads, yes. Batch ETL is fine for hourly dashboards, but CDC streams logical changes as they happen, giving sub-minute freshness without scheduled queries hammering your source. ClickHouse's ReplacingMergeTree engine pairs cleanly with the upsert-style event stream that CDC emits.

How does real-time ClickHouse integration affect source database performance?

Properly configured CDC reads from the write-ahead log (Postgres WAL) or binlog (MySQL), not the primary tables, so load is minimal. Usually under 5% CPU on a healthy source. The real risk is unbounded replication slot growth if the consumer falls behind, so pick a tool with backpressure handling and slot monitoring built in.

Try It

ClickHouse Cloud is live as an Artie destination today. Spin up a pipeline from Postgres, MySQL, SQL Server, Oracle, or MongoDB and you'll see first rows land in ClickHouse within seconds. Start with the ClickHouse destination docs, or book a 20-minute walkthrough if you'd rather we wire up your source live.

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