ClickHouse and Databricks represent different philosophies in modern data analytics. ClickHouse is a high-performance columnar database optimised for real-time analytical queries, while Databricks provides a unified lakehouse platform combining data engineering, data science, and SQL analytics. This comparison helps you understand when each platform excels and how to choose between them.
Platform Overview
ClickHouse
ClickHouse is an open-source columnar database management system designed for online analytical processing (OLAP). Originally developed at Yandex for web analytics, it processes petabytes of data with sub-second query latency.
Core strengths:
- Fastest query performance for structured analytical data
- Real-time data ingestion and querying
- Exceptional compression (10-20x)
- Cost-effective at scale
- Open source with commercial cloud offering
Databricks
Databricks is a unified data analytics platform built on Apache Spark, offering a lakehouse architecture that combines data lake flexibility with data warehouse performance.
Core strengths:
- Unified platform for data engineering, ML, and analytics
- Delta Lake for reliable data lakes
- Collaborative notebooks for data science
- Strong governance and security features
- Deep integration with major cloud providers
Architecture Comparison
ClickHouse Architecture
┌─────────────────────────────────────────────┐
│ ClickHouse Cluster │
├─────────────┬─────────────┬─────────────────┤
│ Shard 1 │ Shard 2 │ Shard N │
│ ┌───────┐ │ ┌───────┐ │ ┌───────┐ │
│ │Replica│ │ │Replica│ │ │Replica│ │
│ │ 1 │ │ │ 1 │ │ │ 1 │ │
│ └───────┘ │ └───────┘ │ └───────┘ │
│ ┌───────┐ │ ┌───────┐ │ ┌───────┐ │
│ │Replica│ │ │Replica│ │ │Replica│ │
│ │ 2 │ │ │ 2 │ │ │ 2 │ │
│ └───────┘ │ └───────┘ │ └───────┘ │
└─────────────┴─────────────┴─────────────────┘
│ │ │
└──────────────┼──────────────┘
│
MergeTree Storage
(Columnar, Compressed)Key components:
- Shared-nothing distributed architecture
- MergeTree table engine with sorted, partitioned storage
- Distributed query execution across shards
- ZooKeeper/ClickHouse Keeper for coordination
Databricks Architecture
┌─────────────────────────────────────────────┐
│ Databricks Workspace │
├─────────────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ ┌─────────────┐ │
│ │ Delta │ │ ML │ │ SQL │ │
│ │ Live │ │ Runtime │ │ Warehouse │ │
│ │ Tables │ │ │ │ │ │
│ └────┬────┘ └────┬────┘ └──────┬──────┘ │
│ │ │ │ │
│ └────────────┼──────────────┘ │
│ │ │
│ ┌────────▼────────┐ │
│ │ Delta Lake │ │
│ │ (Parquet + Tx) │ │
│ └────────┬────────┘ │
└────────────────────┼────────────────────────┘
│
Cloud Object Storage (S3/ADLS/GCS)Key components:
- Unity Catalog for governance
- Delta Lake for ACID transactions on data lakes
- Photon engine for accelerated SQL
- Auto-scaling compute clusters
Performance Comparison
Query Performance
| Query Type | ClickHouse | Databricks SQL |
|---|---|---|
| Simple aggregation (1B rows) | 0.5-2s | 5-15s |
| Complex JOIN | 2-10s | 10-60s |
| Time-series rollup | 0.1-1s | 3-10s |
| Ad-hoc exploration | Sub-second | 5-30s |
| Concurrent queries (100+) | Excellent | Good |
ClickHouse advantages:
- Purpose-built for analytical queries
- Vectorised execution optimised for modern CPUs
- Data always hot in optimised columnar format
- Minimal query startup overhead
Databricks advantages:
- Better for extremely large joins across tables
- Handles semi-structured data (JSON, nested) natively
- Photon engine narrows the gap for SQL workloads
- Better for complex transformations
Data Ingestion
| Aspect | ClickHouse | Databricks |
|---|---|---|
| Real-time streaming | Native (Kafka, etc.) | Structured Streaming |
| Batch loading | Very fast | Fast |
| Latency to query | Milliseconds | Seconds to minutes |
| Data formats | Own format, Parquet | Delta, Parquet, JSON, etc. |
Feature Comparison
| Feature | ClickHouse | Databricks |
|---|---|---|
| Query language | SQL (extended) | SQL, Python, Scala, R |
| Real-time analytics | Excellent | Good |
| Machine learning | Limited | Excellent (MLflow) |
| Data engineering | Basic | Excellent (Spark) |
| Data governance | Basic | Unity Catalog |
| Notebooks | No | Yes |
| Version control | No | Delta Lake time travel |
| Semi-structured data | JSON columns | Native nested types |
| Streaming | Kafka integration | Structured Streaming |
Cost Comparison
ClickHouse (Self-Managed)
Infrastructure costs only:
- Compute: $0.05-0.15 per GB processed
- Storage: $0.02-0.03 per GB/month (compressed)
- No licensing fees (open source)ClickHouse Cloud
- Compute: $0.30-0.50 per compute hour
- Storage: $0.04 per GB/month
- Data transfer: Standard cloud ratesDatabricks
- DBU pricing: $0.07-0.55 per DBU
- Plus underlying cloud compute costs
- SQL Warehouse: $0.22-0.55 per DBU
- Typical total: $0.40-1.00+ per compute hourCost analysis:
- ClickHouse is typically 3-5x cheaper for pure analytical workloads
- Databricks provides more value when using ML and data engineering features
- ClickHouse self-managed offers lowest costs with operational overhead
For cost optimisation strategies, see our AWS cloud cost optimisation guide.
Use Case Recommendations
Choose ClickHouse When:
- Real-time dashboards - Sub-second queries on billions of rows
- Log and event analytics - High-volume ingestion with instant queries
- Time-series workloads - Metrics, monitoring, IoT data
- Cost-sensitive analytics - Maximum performance per dollar
- High concurrency - Hundreds of concurrent dashboard users
Example: Marketing analytics platform
-- Real-time campaign performance
SELECT
campaign_id,
count() AS impressions,
countIf(clicked) AS clicks,
countIf(converted) AS conversions,
sum(revenue) AS total_revenue
FROM ad_events
WHERE event_date >= today() - 7
GROUP BY campaign_id
ORDER BY total_revenue DESC
LIMIT 100Choose Databricks When:
- Unified data platform - Engineering, science, and analytics together
- Machine learning workflows - Training, deployment, monitoring
- Complex ETL pipelines - Multi-step transformations
- Data lake modernisation - Adding reliability to existing lakes
- Collaborative analysis - Notebooks for team exploration
Example: ML feature pipeline
# Delta Live Tables pipeline
@dlt.table
def customer_features():
return (
dlt.read("raw_transactions")
.groupBy("customer_id")
.agg(
F.count("*").alias("transaction_count"),
F.sum("amount").alias("total_spend"),
F.avg("amount").alias("avg_transaction")
)
)Hybrid Architecture
Many organisations use both platforms:
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Sources │────▶│ Databricks │────▶│ ClickHouse │
│ (Raw Data) │ │ (ETL/ML) │ │ (Dashboards)│
└─────────────┘ └─────────────┘ └─────────────┘
│
▼
┌─────────────┐
│ ML Models │
│ (Serving) │
└─────────────┘- Databricks handles data engineering and ML
- ClickHouse serves real-time dashboards
- Best of both worlds for comprehensive analytics
Integration Considerations
ClickHouse Integrations
- Ingestion: Kafka, Kinesis, RabbitMQ, HTTP
- BI Tools: Grafana, Metabase, Superset, Tableau
- Orchestration: Airflow, Dagster, Prefect
- CDC: Debezium, Maxwell, custom
Databricks Integrations
- Cloud native: Deep AWS, Azure, GCP integration
- Data sources: 100+ connectors
- ML tools: MLflow, TensorFlow, PyTorch
- BI Tools: Native SQL interface, Power BI, Tableau
- Governance: Unity Catalog, external metastores
Both platforms integrate with modern observability platforms for monitoring query performance.
Operational Comparison
ClickHouse Operations
Pros:
- Simple to operate once configured
- Predictable performance
- Low resource overhead
Cons:
- Requires understanding of data modelling
- Schema changes need planning
- Self-managed requires expertise
Databricks Operations
Pros:
- Fully managed infrastructure
- Auto-scaling compute
- Integrated monitoring
Cons:
- Can be complex to optimise costs
- Cluster startup latency
- Requires Spark expertise for advanced use
Migration Considerations
From Databricks to ClickHouse
Consider when:
- Queries are primarily analytical aggregations
- Real-time requirements exceed Databricks capabilities
- Cost optimisation is critical
From ClickHouse to Databricks
Consider when:
- Adding ML capabilities to analytics
- Need for complex data transformations
- Unified platform benefits outweigh performance trade-offs
Conclusion
ClickHouse and Databricks serve different primary purposes:
ClickHouse excels at real-time analytical queries with unmatched performance and cost efficiency. Choose it for dashboards, monitoring, and high-concurrency analytical workloads.
Databricks provides a unified platform for data engineering, data science, and SQL analytics. Choose it when you need ML capabilities, complex transformations, and collaborative data work.
Many organisations benefit from using both: Databricks for data engineering and ML, ClickHouse for real-time analytics and dashboards.
Need help building your analytics architecture? Contact our data engineering team to discuss your requirements.
Related Resources
- How Tasrie IT Services Uses ClickHouse
- ClickHouse vs Snowflake 2026
- Cloud Native Database Guide 2026
- Top 10 Observability Platforms
External Resources: