Airbyte + ClickHouse + Grafana
IntermediateDashboardAirbyte ingests data into ClickHouse, Grafana visualizes it in real-time dashboards.
Published 27 September 2026
About Airbyte + ClickHouse + Grafana
Airbyte, ClickHouse, and Grafana form a modern open-source analytics stack optimized for high-volume time-series and event data. Airbyte replicates data from source databases and SaaS tools into ClickHouse, a columnar database purpose-built for fast analytical queries. Grafana connects to ClickHouse and renders real-time dashboards that update as new data arrives.
ClickHouse's architecture allows it to aggregate billions of rows in milliseconds, far faster than PostgreSQL for analytical workloads. Airbyte's 300+ connectors handle data ingestion from cloud services, relational databases, and streaming sources without custom ETL scripts. Grafana's ClickHouse plugin surfaces query results as time-series charts, heatmaps, and tables.
This stack is designed for engineering and data teams who need sub-second analytical dashboard performance on large event or log datasets, and who want to stay on self-hostable open-source components.
Key Features
- ✓Airbyte 300+ connectors replicate data from SaaS and databases to ClickHouse
- ✓ClickHouse columnar storage for billion-row analytical queries in milliseconds
- ✓Grafana ClickHouse plugin for real-time dashboard rendering
- ✓Materialized views in ClickHouse for pre-aggregated metrics
- ✓Airbyte incremental sync for efficient ongoing data replication
- ✓Fully open-source stack, self-hostable with no per-query or per-seat licensing
When to Use Airbyte + ClickHouse + Grafana
- →Real-time product event analytics dashboards for engineering and product teams
- →Log analytics at scale with sub-second query performance on ClickHouse
- →E-commerce analytics ingesting order and clickstream data via Airbyte
- →Marketing attribution analysis combining data from multiple ad platforms
- →Infrastructure monitoring time-series data stored in ClickHouse and visualized in Grafana
Pros
- ClickHouse is among the fastest analytical databases for high-volume queries
- Airbyte covers most data source connectors without custom ETL code
- Fully open-source: all components can be self-hosted at no per-query cost
- Grafana provides flexible, production-quality dashboard visualization
Cons
- Significant operational complexity: three systems to deploy and maintain
- ClickHouse has a steep learning curve with its own SQL dialect and data model
- Airbyte self-hosted requires its own infrastructure separate from the ClickHouse server
Airbyte + ClickHouse + Grafana Add-ons
Each addition below extends this stack with a capability the base stack works fine without. None are required: include the ones your product actually needs when building this stack, and skip the rest.
CI/CD Add-ons
Add CI/CD when you want a dedicated pipeline for running tests, linting, or multi-stage builds before a deploy goes out. Many hosting platforms already redeploy automatically on every push on their own — a CI/CD tool adds the most value on top of that by gating the deploy on a passing test suite, and matters even more when the hosting choice does not auto-deploy at all, such as a self-hosted server.
Airbyte + ClickHouse + Grafana with GitHub Actions
Lints the Python event-processing scripts and validates any ClickHouse schema changes before they reach the tables Grafana dashboards query.
Airbyte + ClickHouse + Grafana with GitLab CI/CD
Covers the same lint-and-validate step for teams running their Python scripts and ClickHouse migrations from a GitLab repo.
These are highlighted picks. To see all the tools, check the CI/CD Pipelines category.
Containerization Add-ons
Add containerization when you want the app packaged the same way across local development, staging, and production, or need to deploy somewhere that isn't a managed serverless platform.
Airbyte + ClickHouse + Grafana with Docker
Package the Python event-processing scripts and the self-hosted ClickHouse/Grafana instances identically across a laptop, staging, and a production server.
These are highlighted picks. To see all the tools, check the Containerization category.
Frequently Asked Questions about Airbyte + ClickHouse + Grafana
ClickHouse or Snowflake for this kind of pipeline?
ClickHouse is the right call when queries need to be fast and cheap at high event volume and the workload is real-time or near-real-time analytics. Snowflake fits better for classic BI reporting over structured business data with less emphasis on sub-second query speed.
Do I need dbt for transformations here?
Not necessarily. ClickHouse's materialized views can handle a lot of the pre-aggregation directly; dbt becomes worth adding once transformation logic gets complex enough to want version control, tests, and documentation around it.
What does it take to run all three services?
More than a minimal VPS. Airbyte is the heaviest to install: its self-hosted setup runs on a small Kubernetes cluster that it creates for you, and its quickstart asks for roughly 4 CPUs and 8 GB of RAM before anything else is running. ClickHouse is light when idle but uses CPU and memory in bursts during ingestion and large queries, and it needs fast disk. Grafana is small. A single server with 8 CPUs and 16 GB of RAM runs all three for a modest event volume; beyond that, give ClickHouse its own machine so a big sync or a heavy dashboard query can't starve the others. Back up ClickHouse's data; Airbyte's configuration and Grafana's dashboards are small and easy to export.
Why do dashboards show duplicate rows after a deduplicated sync?
Because ClickHouse deduplicates later, not on insert. For incremental syncs with deduplication, rows land in tables built on ReplacingMergeTree, which keeps every inserted version and only drops older copies when ClickHouse merges data parts in the background. Until that merge happens, a plain count or sum can include the same record twice. Queries that must be exact can add FINAL to the table reference, which deduplicates at read time at some cost in speed, or aggregate on the newest version of each key. For dashboards, the usual answer is a materialized view that maintains the aggregate on the fly, so Grafana reads small, already-correct tables.
How do I keep Grafana dashboards fast on large ClickHouse tables?
Make every panel's query respect the dashboard's time range, using the ClickHouse plugin's time-filter macro on a column that is part of the table's sort key, so ClickHouse reads only the relevant slice rather than scanning the full history. Keep auto-refresh reasonable: a dashboard of twenty panels refreshing every five seconds sends thousands of queries an hour, even with nobody watching. For the charts that everyone opens, query pre-aggregated materialized views (per minute or per hour) instead of raw events. Those three habits keep query times low as the tables grow from millions to billions of rows.
Stacks Related to Airbyte + ClickHouse + Grafana
Scores
Tools in the Airbyte + ClickHouse + Grafana Stack
Programming Languages
Databases
Data Engineering & ETL
Observability & Monitoring
Add-ons (optional — add any, or none)
CI/CD
Containerization
Airbyte + ClickHouse + Grafana Pricing
Airbyte, ClickHouse, and Grafana are all free and open source in their self-hosted editions used here. The only recurring cost is the server running them; ClickHouse in particular benefits from real CPU and memory headroom once event volume grows past a hobby-scale workload.
All three are free to self-host with no usage limits; Airbyte Cloud starts at $10/mo if managed hosting is preferred.
Airbyte's self-hosted install alone asks for roughly 4 CPUs and 8 GB of RAM, and ClickHouse wants CPU and memory headroom for continuous ingestion; plan on one mid-size server or separate small ones rather than a minimal VPS.