ClickHouse and BigQuery are both powerful analytical databases, but they differ significantly in architecture, pricing, and operational models. BigQuery offers a serverless experience with Google Cloud integration, while ClickHouse provides superior query performance with more predictable costs. This comparison helps you choose the right platform for your analytics needs.
Platform Overview
ClickHouse
ClickHouse is an open-source columnar database available as self-managed software or ClickHouse Cloud, optimised for real-time analytical queries.
Key characteristics:
- Fastest query performance in class
- Predictable, usage-based pricing
- Real-time data ingestion
- Open source with cloud option
- Multi-cloud deployment
BigQuery
BigQuery is Google’s fully managed, serverless data warehouse with built-in machine learning and BI capabilities.
Key characteristics:
- Serverless, zero operations
- Deep GCP ecosystem integration
- Built-in ML (BigQuery ML)
- Automatic scaling
- Pay-per-query pricing model
Architecture Comparison
ClickHouse Architecture
┌─────────────────────────────────────────┐
│ ClickHouse Cluster │
├─────────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ ┌─────────┐ │
│ │ Shard 1 │ │ Shard 2 │ │ Shard N │ │
│ │ Node │ │ Node │ │ Node │ │
│ └────┬────┘ └────┬────┘ └────┬────┘ │
│ │ │ │ │
│ └────────────┼────────────┘ │
│ │ │
│ Local SSD Storage │
│ (Columnar, Compressed) │
└─────────────────────────────────────────┘BigQuery Architecture
┌─────────────────────────────────────────┐
│ BigQuery │
├─────────────────────────────────────────┤
│ ┌─────────────────────────────────┐ │
│ │ Dremel Engine │ │
│ │ (Distributed Query Execution) │ │
│ └───────────────┬─────────────────┘ │
│ │ │
│ ┌───────────────▼─────────────────┐ │
│ │ Colossus │ │
│ │ (Distributed Storage Layer) │ │
│ └─────────────────────────────────┘ │
│ │
│ Separation of Compute/Storage │
└─────────────────────────────────────────┘Performance Comparison
Query Performance
| Query Type | ClickHouse | BigQuery |
|---|---|---|
| Simple aggregation (1B rows) | 0.5-1s | 3-8s |
| Complex GROUP BY | 1-2s | 5-15s |
| Time-series rollup | 0.3s | 2-5s |
| Large JOIN | 3-5s | 10-30s |
| Concurrent queries (50+) | Consistent | Variable |
ClickHouse advantages:
- 3-10x faster query execution
- No cold start latency
- Consistent performance under load
- Better for real-time dashboards
BigQuery advantages:
- Handles extremely large datasets (petabytes)
- No infrastructure management
- Automatic query optimisation
- Slot-based scaling for predictable performance
Query Cold Start
| Scenario | ClickHouse | BigQuery |
|---|---|---|
| First query | Immediate | 1-3s slot allocation |
| Cached query | Very fast | Fast (cache hit) |
| Large scan | Proportional | Slot-dependent |
Pricing Comparison
ClickHouse Cloud Pricing
Compute: ~$0.30-0.50 per compute hour
Storage: ~$0.04 per GB/month
Ingestion: Included
Data transfer: Standard cloud ratesBigQuery Pricing
On-demand:
- Analysis: $6.25 per TB scanned
- Storage: $0.02 per GB/month (active)
- Storage: $0.01 per GB/month (long-term)
Flat-rate (slots):
- $2,000/month per 100 slots
- Predictable but requires commitmentCost Scenarios
| Scenario | ClickHouse Cloud | BigQuery On-Demand |
|---|---|---|
| 10TB stored, 100TB scanned/month | ~$1,500/month | ~$625 + storage |
| 100TB stored, 1PB scanned/month | ~$5,000/month | ~$6,250 + storage |
| Real-time dashboards (always-on) | Predictable | Can be expensive |
| Sporadic queries | May over-provision | Cost-efficient |
Cost analysis:
- BigQuery excels for sporadic, large-scale queries
- ClickHouse is more cost-effective for continuous workloads
- BigQuery on-demand can surprise with large scans
- ClickHouse provides predictable costs
For cloud cost strategies, see our AWS cost optimisation guide.
Feature Comparison
| Feature | ClickHouse | BigQuery |
|---|---|---|
| Query language | SQL (extended) | Standard SQL |
| Real-time ingestion | Excellent | Good (streaming) |
| Serverless | Cloud option | Fully serverless |
| ML integration | Limited | BigQuery ML |
| BI integration | Grafana, etc. | Looker, Data Studio |
| Geospatial | Basic | Excellent (GIS) |
| Semi-structured | Good | Excellent |
| Data sharing | Basic | Analytics Hub |
| Multi-cloud | Yes | GCP only |
| Open source | Yes | No |
Use Case Recommendations
Choose ClickHouse When:
Real-time analytics and dashboards
-- Sub-second queries for live dashboards
SELECT
toStartOfMinute(event_time) AS minute,
count() AS events,
uniqExact(user_id) AS users,
quantile(0.95)(latency_ms) AS p95_latency
FROM events
WHERE event_time >= now() - INTERVAL 1 HOUR
GROUP BY minute
ORDER BY minute DESCHigh-frequency queries with predictable costs
- Always-on dashboard serving
- High query concurrency
- Cost-sensitive workloads
Multi-cloud or cloud-agnostic requirements
- Avoid GCP lock-in
- Self-hosted option needed
- Hybrid cloud deployments
Log analytics and observability
- High-volume log ingestion
- Real-time monitoring
- Time-series metrics
Choose BigQuery When:
Serverless data warehousing
-- Leverage BigQuery ML
CREATE OR REPLACE MODEL `project.dataset.churn_model`
OPTIONS(model_type='logistic_reg') AS
SELECT
customer_features.*,
churned
FROM `project.dataset.customer_features`;Deep GCP ecosystem integration
- Pub/Sub streaming ingestion
- Dataflow transformations
- Looker visualisation
- Vertex AI integration
Ad-hoc analysis with variable workloads
- Sporadic large queries
- Data exploration
- Pay-per-query efficiency
Geospatial analytics
-- BigQuery GIS capabilities
SELECT
store_id,
ST_DISTANCE(
ST_GEOGPOINT(store_lon, store_lat),
ST_GEOGPOINT(customer_lon, customer_lat)
) AS distance_meters
FROM stores, customers
WHERE ST_DWITHIN(
ST_GEOGPOINT(store_lon, store_lat),
ST_GEOGPOINT(customer_lon, customer_lat),
10000 -- 10km radius
)Integration Ecosystem
ClickHouse Integrations
Data ingestion:
- Kafka, Kinesis direct
- Vector, Fluent Bit
- dbt adapter
- Airbyte, Fivetran
Visualisation:
- Grafana (excellent)
- Metabase, Superset
- Tableau, Looker
Orchestration:
- Airflow, Dagster
- Prefect
BigQuery Integrations
GCP native:
- Pub/Sub, Dataflow
- Cloud Functions
- Vertex AI
- Looker, Data Studio
Third-party:
- All major ETL tools
- All major BI tools
- dbt (first-class support)
Operational Comparison
ClickHouse Operations
Self-managed:
- Full control
- Operational overhead
- Maximum flexibility
ClickHouse Cloud:
- Managed infrastructure
- Still requires some configuration
- Multi-cloud options
BigQuery Operations
- Zero infrastructure management
- Automatic scaling and optimisation
- No operational overhead
- Google handles everything
Query Language Differences
ClickHouse SQL Extensions
-- ClickHouse-specific functions
SELECT
toStartOfHour(timestamp) AS hour,
arrayJoin(tags) AS tag,
countIf(status = 'error') AS errors,
quantileTDigest(0.99)(latency) AS p99
FROM events
WHERE timestamp >= now() - INTERVAL 24 HOUR
GROUP BY hour, tag
HAVING errors > 10BigQuery Standard SQL
-- BigQuery syntax
SELECT
TIMESTAMP_TRUNC(timestamp, HOUR) AS hour,
tag,
COUNTIF(status = 'error') AS errors,
APPROX_QUANTILES(latency, 100)[OFFSET(99)] AS p99
FROM events,
UNNEST(tags) AS tag
WHERE timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24 HOUR)
GROUP BY hour, tag
HAVING errors > 10Migration Considerations
BigQuery to ClickHouse
Consider when:
- Query performance is critical
- Costs are becoming unpredictable
- Multi-cloud strategy needed
-- Export from BigQuery
EXPORT DATA OPTIONS(
uri='gs://bucket/export/*.parquet',
format='PARQUET'
) AS
SELECT * FROM dataset.table;
-- Import to ClickHouse
INSERT INTO table
SELECT * FROM s3('https://storage.googleapis.com/bucket/export/*.parquet', 'Parquet')ClickHouse to BigQuery
Consider when:
- Need serverless operations
- Heavy GCP ecosystem usage
- BigQuery ML requirements
Conclusion
ClickHouse and BigQuery serve different operational models:
Choose ClickHouse for real-time analytics, predictable costs, multi-cloud flexibility, and scenarios where query performance is paramount. It excels at always-on workloads with high query volumes.
Choose BigQuery for serverless operations, deep GCP integration, variable workloads, and when built-in ML or geospatial capabilities are needed. It’s ideal when you want zero operational overhead.
Consider your workload pattern: Continuous, high-frequency queries favour ClickHouse. Sporadic, large-scale analysis may favour BigQuery’s pay-per-query model.
For help choosing the right analytics platform, contact our team to discuss your requirements.
Related Resources
- How Tasrie IT Services Uses ClickHouse
- ClickHouse vs Snowflake 2026
- ClickHouse vs Redshift 2026
- Cloud Native Database Guide 2026
External Resources: