Choosing between ClickHouse and BigQuery is one of the most consequential decisions for your analytics infrastructure. Both are powerful analytical databases, but they serve different needs and come with distinct trade-offs in performance, cost, and operational complexity.

This guide provides an in-depth comparison to help you make the right choice for your event analytics workload.

Architecture Overview

Understanding the fundamental architecture differences is essential for making an informed decision.

ClickHouse Architecture

ClickHouse is an open-source, columnar OLAP database designed for real-time analytics:

  • Self-hosted or managed: Deploy on your infrastructure or use ClickHouse Cloud
  • Coupled storage and compute: Data and processing reside on the same nodes, reducing network overhead for faster query times
  • Real-time ingestion: Native support for streaming inserts with immediate queryability
  • MergeTree engine: Unique storage engine optimized for analytical queries with background merging of data parts
  • Vectorized execution: Processes data in columnar blocks, maximizing CPU cache efficiency and SIMD instructions
  • SQL dialect: Extended SQL with analytical functions and syntax

BigQuery Architecture

BigQuery is Google Cloud's fully managed, serverless data warehouse:

  • Fully serverless: No infrastructure management required
  • Separation of storage and compute: Independent scaling of each layer via Google's high-speed network
  • Dremel execution engine: Distributed query processing across thousands of workers using slots (virtual CPUs)
  • Capacitor format: Proprietary columnar storage with automatic optimization and compression
  • Standard SQL: ANSI-compliant SQL with extensions
  • Slot-based resource allocation: Compute resources measured in slots; BigQuery determines allocation automatically in on-demand mode

Performance Comparison

Performance characteristics differ significantly between the two systems.

Query Latency

ClickHouse strengths:

  • Sub-second queries on properly indexed tables, even on multi-billion-row datasets
  • Consistent low latency for repetitive queries
  • Excellent for real-time dashboards and monitoring
  • No cold start or slot allocation delays
  • Local disk reads eliminate network I/O overhead during query execution

BigQuery characteristics:

  • Typical query latency: 1-30 seconds for most queries
  • Minimum latency floor around 1-2 seconds due to job scheduling and resource allocation
  • BI Engine cache can reduce latency for repeated queries (in-memory acceleration)
  • Better suited for ad-hoc analysis and batch workloads than real-time dashboards
  • Performance depends on slot availability in shared pool (on-demand) or reserved capacity

Throughput and Scale

ClickHouse:

  • Handles millions of inserts per second per node
  • Linear scaling with cluster size for both reads and writes
  • Excellent for high-frequency event streams
  • Performance depends on cluster sizing and tuning
  • Supports 1,000+ concurrent queries per node with proper configuration

BigQuery:

  • Streaming inserts: up to 1 million rows per second per table (with Storage Write API)
  • Batch loads: virtually unlimited throughput (and free when using shared pool)
  • Auto-scales compute resources per query up to 2,000 slots per project (on-demand)
  • No upper limit on data volume
  • Concurrency limited by slot availability; default 100 concurrent queries per project

Benchmark Considerations

-- Typical query patterns and expected performance

-- Point lookup (ClickHouse: <10ms, BigQuery: 1-3s)
SELECT * FROM events WHERE event_id = 'abc123';

-- Time-series aggregation (ClickHouse: 50-500ms, BigQuery: 2-10s)
SELECT date, count(*) FROM events
WHERE event_time >= '2025-01-01'
GROUP BY date;

-- Complex analytics (ClickHouse: 1-5s, BigQuery: 5-30s)
SELECT user_id, funnel_steps...
FROM events
WHERE ... complex joins and window functions;

Note: Actual performance varies significantly based on data volume, schema design, indexing, and cluster/slot configuration. Always benchmark with your own workloads.

Cost Analysis

Cost structures are fundamentally different and require careful analysis for your specific workload.

BigQuery Pricing Model

On-demand pricing:

  • Query processing: $6.25 per TiB scanned (first 1 TiB per month free)
  • Storage (logical): $0.02/GiB/month (active), $0.01/GiB/month (long-term after 90 days)
  • Storage (physical): $0.04/GiB/month (active), $0.02/GiB/month (long-term)
  • Streaming inserts (legacy API): $0.01 per 200 MiB
  • Storage Write API: $0.025 per GiB (first 2 TiB per month free)
  • Batch loading: Free when using shared slot pool

Capacity pricing (BigQuery Editions):

  • Standard Edition: $0.04/slot-hour (pay-as-you-go only)
  • Enterprise Edition: $0.06/slot-hour (PAYG), $0.048/slot-hour (1-year), $0.036/slot-hour (3-year)
  • Enterprise Plus: $0.10/slot-hour (PAYG), $0.08/slot-hour (1-year), $0.06/slot-hour (3-year)
  • Minimum 50 slots, billed per second with 1-minute minimum
  • Autoscaling available to dynamically adjust capacity

Cost optimization strategies:

  • Partition and cluster tables to reduce scanned data
  • Use materialized views for repeated queries
  • Consider Editions pricing for predictable heavy workloads
  • Batch loads are free; prefer them over streaming when latency permits
  • Use physical storage billing for highly compressible data

ClickHouse Pricing Model

Self-hosted costs:

  • Infrastructure: VMs, storage, networking
  • Operations: Engineering time for maintenance (typically 0.25-1 FTE)
  • Typical production cluster: $2,000-10,000/month on cloud VMs
  • No per-query or per-byte charges

ClickHouse Cloud (as of 2025):

  • Storage: $25.30 per TiB/month (~$0.025/GiB)
  • Compute: $0.22-0.39 per compute unit-hour (varies by tier and region)
  • Three tiers: Basic, Scale, and Enterprise with increasing features
  • Auto-scaling and auto-pause to zero (pay only when active)
  • ClickPipes ingestion: $0.04/GB ingested + $0.20/hr per compute unit

Cost Comparison Example

-- Scenario: 1TB raw events/month, 50TB scanned/month in queries, moderate streaming

BigQuery On-Demand:
  Storage: 1TB * $0.02 = $20/month
  Queries: 50TB * $6.25 = $312.50/month
  Streaming (Write API, after free tier): ~$50/month
  Total: ~$380/month

BigQuery Enterprise Edition (100 slots baseline):
  Slots: 100 * $0.06 * 720 hours = $4,320/month
  Storage: $20/month
  Total: ~$4,340/month (but predictable, unlimited queries)

ClickHouse Cloud Scale tier (estimated):
  Compute: ~$300-600/month (with auto-pause)
  Storage: 1TB * $25.30 = $25.30/month
  Total: ~$325-625/month

Self-hosted ClickHouse (3-node on AWS):
  EC2 (m6i.2xlarge): 3 * $280 = $840/month
  EBS Storage: ~$100/month
  Engineering time: Variable (0.25-0.5 FTE)
  Total: ~$940/month + ops overhead

Note: Actual costs vary significantly by workload patterns, region, and usage. Use official pricing calculators for accurate estimates.

Operational Complexity

The operational burden differs dramatically between managed and self-hosted options.

BigQuery Operations

Advantages:

  • Zero infrastructure management
  • Automatic scaling and performance optimization
  • Built-in high availability and disaster recovery
  • No capacity planning required for on-demand pricing
  • Integrated security and compliance (SOC 2, HIPAA, FedRAMP, etc.)
  • Automatic software updates and maintenance

Considerations:

  • Limited control over query execution and resource allocation
  • Vendor lock-in to Google Cloud ecosystem
  • Debugging performance issues can be challenging (limited visibility into slots)
  • Cost unpredictability with on-demand pricing at scale

ClickHouse Operations (Self-hosted)

Requirements:

  • Cluster deployment and configuration
  • Monitoring and alerting setup
  • Backup and disaster recovery planning
  • Version upgrades and security patches
  • Performance tuning and capacity planning
  • Replication and sharding management
  • Schema design expertise (primary keys, partitioning, projections)

Typical team requirements:

  • Small deployment: 0.25-0.5 FTE for operations
  • Large deployment: 1-2 FTEs dedicated to ClickHouse
  • Requires database engineering expertise

ClickHouse Cloud Operations

Reduces operational burden significantly:

  • Managed infrastructure and automatic updates
  • Automatic backups and replication
  • Built-in monitoring and observability
  • Still requires schema design and query optimization expertise
  • More control than BigQuery, less than self-hosted
  • Scale and Enterprise tiers offer additional features (private networking, CMEK, HIPAA compliance)

Feature Comparison

Data Ingestion

Feature ClickHouse BigQuery
Real-time streaming Native, immediate queryability Storage Write API, slight delay (~seconds)
Batch loading Multiple formats (Parquet, CSV, JSON, etc.) Multiple formats, free loading via shared pool
CDC support Via Kafka, ClickPipes, Debezium integration Datastream, BigQuery Data Transfer Service
Ingestion throughput Millions of rows/second per node Up to 1M rows/second per table (streaming)

Query Capabilities

ClickHouse advantages:

  • Approximate aggregation functions (uniq, uniqExact, quantile, quantileTDigest)
  • Array and nested data type handling with powerful functions
  • Powerful time-series functions and date/time manipulation
  • PREWHERE for optimized filtering before main WHERE clause
  • Sampling for fast approximate results on large datasets
  • Projections for pre-aggregated query acceleration
  • Multiple compression codecs (LZ4, ZSTD, Delta, DoubleDelta)

BigQuery advantages:

  • Native ML with BigQuery ML (train models with SQL)
  • Geospatial analytics (BigQuery GIS)
  • BI Engine for dashboard acceleration (in-memory cache)
  • Seamless integration with Google ecosystem (Looker, Data Studio, Vertex AI)
  • Scheduled queries and data transfer service
  • ANSI SQL compliance with fewer dialect differences
  • Federated queries to external sources (BigLake, Cloud SQL)

Ecosystem Integration

ClickHouse:

  • Works with any BI tool via JDBC/ODBC/native drivers
  • Native integrations: Grafana, Metabase, Superset, Tableau
  • 70+ supported file formats and external table engines
  • Kafka, S3, GCS, and file-based connectors
  • Cloud-agnostic deployment (AWS, GCP, Azure, on-premises)
  • External table engines for Postgres, MySQL, MongoDB, S3

BigQuery:

  • Deep Google Cloud integration (GCS, Dataflow, Pub/Sub, Looker)
  • Connected Sheets for spreadsheet access
  • BigQuery Omni for multi-cloud queries (AWS, Azure)
  • Data Catalog for governance and discovery
  • Vertex AI integration for ML workflows
  • BigLake for unified data lake queries

When to Choose ClickHouse

ClickHouse is the better choice when:

  1. Real-time requirements: You need sub-second query latency for dashboards or monitoring
  2. High-volume event streams: Ingesting millions of events per second with immediate queryability
  3. Cost sensitivity at scale: Query volume makes BigQuery on-demand prohibitively expensive
  4. Multi-cloud strategy: You want to avoid vendor lock-in to a single cloud
  5. Custom requirements: You need fine-grained control over storage, compression, and performance tuning
  6. High concurrency: Powering customer-facing applications with thousands of concurrent queries
  7. Existing expertise: Your team has database engineering experience

Ideal ClickHouse Use Cases

  • Real-time product analytics dashboards
  • Application performance monitoring (APM)
  • Log analysis and observability (ClickStack)
  • Ad-tech and real-time bidding
  • IoT sensor data analysis
  • Customer-facing data applications
  • Gaming analytics and leaderboards

When to Choose BigQuery

BigQuery is the better choice when:

  1. Minimal operations: You want zero infrastructure management
  2. Variable workloads: Query volume is unpredictable or bursty
  3. Google Cloud ecosystem: You're already invested in GCP services
  4. Ad-hoc analysis: Primary use is exploratory analytics, not real-time dashboards
  5. ML integration: You want native machine learning capabilities with BigQuery ML
  6. Small to medium scale: Query costs are manageable with on-demand pricing
  7. Compliance requirements: You need built-in certifications (HIPAA, FedRAMP, PCI)

Ideal BigQuery Use Cases

  • Data warehousing and business intelligence
  • Ad-hoc exploratory analysis
  • Machine learning on structured data
  • Marketing analytics and attribution
  • Financial reporting and compliance
  • Data lake analytics with BigLake
  • Multi-cloud analytics with BigQuery Omni

Hybrid Approaches

Many organizations use both systems for different purposes:

Common Hybrid Patterns

  • ClickHouse for real-time, BigQuery for historical: Stream to ClickHouse for dashboards, batch to BigQuery for deep analysis
  • ClickHouse for hot data, BigQuery for cold: Keep recent data in ClickHouse, archive older data to BigQuery
  • ClickHouse for events, BigQuery for warehouse: Use ClickHouse for event analytics, BigQuery for joining with other business data
  • ClickHouse for customer-facing, BigQuery for internal: Power user applications with ClickHouse, run internal BI on BigQuery

Data Synchronization

-- Export from ClickHouse to GCS for BigQuery
INSERT INTO FUNCTION s3(
  'gs://bucket/events/*.parquet',
  'Parquet'
)
SELECT * FROM events
WHERE event_date = today() - 1;

-- BigQuery external table from GCS
CREATE EXTERNAL TABLE events_archive
OPTIONS (
  format = 'PARQUET',
  uris = ['gs://bucket/events/*.parquet']
);

-- Or use BigQuery Data Transfer Service for scheduled loads

Migration Considerations

From BigQuery to ClickHouse

  • Export data via GCS in Parquet format
  • Redesign schema for MergeTree optimization (primary keys, partitioning, projections)
  • Rewrite queries for ClickHouse SQL dialect (minor differences)
  • Plan for increased operational responsibility
  • Consider ClickHouse Cloud for reduced operational burden
  • Test query performance with representative workloads

From ClickHouse to BigQuery

  • Export via S3/GCS compatible storage in Parquet format
  • Adapt to BigQuery's partitioning model (time-based or integer range)
  • Update applications for higher query latency (seconds vs. milliseconds)
  • Migrate scheduled jobs to BigQuery scheduled queries
  • Review cost implications of on-demand vs. Editions pricing

Summary

The choice between ClickHouse and BigQuery depends on your specific requirements:

Criteria ClickHouse BigQuery
Query latency Sub-second (milliseconds) Seconds (1-30s typical)
Operational burden Medium to High (self-hosted) / Low (Cloud) Very Low (fully managed)
Cost model Predictable (infrastructure-based) Variable (on-demand) or predictable (Editions)
Best for Real-time analytics, high concurrency Ad-hoc analysis, batch workloads
Vendor lock-in Low (open-source, multi-cloud) High (GCP ecosystem)
  • Choose ClickHouse for real-time analytics, high-volume event streams, customer-facing applications, and when you need sub-second query performance
  • Choose BigQuery for serverless simplicity, ad-hoc analysis, ML workloads, and deep Google Cloud integration
  • Consider both when you have distinct real-time and batch analytics needs

Evaluate based on your query latency requirements, data volume, cost constraints, and operational capacity. The right choice will serve your analytics needs for years to come.