ON THIS PAGE
## Architecture
The biggest difference among cloud data warehouses are whether they separate storage and compute, how much they isolate data and compute, and what clouds they can run on.
| Feature | BigQuery | ClickHouse |
|---|---|---|
| Separation of storage and compute | Fully serverless with complete separation of compute (Dremel) and storage (Colossus), powered by Jupiter network and Borg orchestration | Yes – SharedMergeTree engine in ClickHouse Cloud enables full separation of storage and compute, with compute-compute separation through Warehouses feature (introduced 2025) allowing multiple isolated compute services sharing the same data |
| Supported cloud infrastructure | Google Cloud only | AWS, GCP, Azure, cloud service and on-premises |
| Isolated tenancy – option for dedicated resources | • Multi-tenant pooled resources • VPC Service Controls provide enhanced security and connectivity isolation to customer VPCs • Cross-region disaster recovery for enterprise workloads | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client in cloud |
| Control vs abstraction of compute | Fully serverless with no control over compute resources – BigQuery automatically allocates computing resources as needed with intelligent workload management and dynamic slot allocation | Configurable cluster size and compute types in ClickHouse Cloud with granular control over nodes (1-128 nodes) and node characteristics. Warehouses feature enables multiple isolated read-only compute environments. |
| Self-hosted and hybrid deployment options | No self-hosted options – fully managed service only | Self-managed deployments available with full control over infrastructure |
| ACID Compliance and Transactions | Limited ACID support – eventual consistency model with some transactional capabilities | Limited ACID compliance with MergeTree engine family. |
BigQuery was one of the first decoupled storage and compute architectures. It is a unique piece of engineering and not a typical data warehouse in part because it started as an on-demand serverless query engine. It runs in multi-tenancy with shared resources, allocated as "slots" which represent a virtual CPU that executes SQL. BigQuery determines how many slots a query requires, without the ability of the user to control it. BigQuery can be priced on a $/TB scanned basis or through slot reservations. A slot in BigQuery is logically equivalent to 0.5 vCPU and 0.5GB of RAM. There are multiple models to allocate slots in BigQuery.
ClickHouse was originally developed at Yandex, the Russian search engine, as an OLAP engine for low latency analytics. It was built as an on-premise solution with coupled storage & compute, and a large variety of tuning options in the form of indexes and and merge trees. ClickHouse's architecture is famous for its focus on performance and low-latency queries. The tradeoff is that it is considered very difficult to work with. SQL support is very limited, and tuning/running it requires significant engineering resources.
## Scalability
There are three big differences among data warehouses and query engines that limit scalability: decoupled storage and compute, dedicated resources, and continuous ingestion.
| Feature | BigQuery | ClickHouse |
|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | Fully automated serverless scaling – BigQuery automatically determines resource allocation and scales to petabytes without user intervention. Can dynamically burst beyond baseline slot allocations for performance optimization | Automatic horizontal and vertical scaling in ClickHouse Cloud with SharedMergeTree architecture. Manual scaling for self-managed deployments with cluster rebalancing capabilities |
| Elasticity – Scaling for higher concurrency | Dynamic concurrency management with query queueing supporting up to 1,000 interactive queries and 20,000 batch queries per project per region. Automatic fair scheduling and slot distribution across workloads | Supports high concurrency with proper resource allocation and configuration. Vertical auto-scaling and horizontal manual scaling. Additional warehouses can idle to zero billing. Primary service always on in multi-warehouse configurations. |
BigQuery scales very well to large data volumes, and automatically assigns more compute resources when needed behind the scenes, in the form of "slots". BigQuery works either in an "on-demand pricing model", where slot assignment is completely in the hands of BigQuery and the state of the shared resource pool, or in "flat-rate pricing model" where slots are reserved in advanced. With reserved slots there is more control over compute resources, thus making scaling more predictable. Concurrency is limited to 100 users by default.
ClickHouse doesn't offer any dedicated scaling features or mechanisms. While it can deliver linearly scalable performance for some types of queries, scaling itself has to be done manually. Hardware is self-managed in ClickHouse. This means that to scale you would have to provision a cluster and migrate.
## Performance
Performance is the biggest challenge with most data warehouses today. While decoupled storage and compute architectures improved scalability and simplified administration, for most data warehouses it introduced two bottlenecks; storage, and compute. Most modern cloud data warehouses fetch entire partitions over the network instead of just fetching the specific data needed for each query. While many invest in caching, most do not invest heavily in query optimization. Most vendors also have not improved continuous ingestion or semi-structured data analytics performance, both of which are needed for operational and customer-facing use cases.
| Feature | BigQuery | ClickHouse |
|---|---|---|
| Indexes | Search indexes (GA) for efficient text search optimization on STRING, JSON, and array columns. Support for LOG_ANALYZER, NO_OP_ANALYZER, and PATTERN_ANALYZER with column-level granularity for improved query performance and cost efficiency | • Primary indexes • Skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • MergeTree indexes • Incremental Materialized views |
| Compute tuning | Serverless architecture with automatic resource optimization – no manual tuning required. Intelligent workload management with AI-powered resource allocation and dynamic slot distribution | Configurable compute resources in cloud offering |
| Storage format | Columnar & compressed storage (Capacitor format) with support for open table formats including Apache Iceberg, Delta Lake, and Hudi. Intelligent tiering with automatic long-term storage cost reduction after 90 days | Columnar, supports sorted, compressed, encoded & sparsely indexed files with native Apache Iceberg support. |
| Table-level partition & pruning techniques | • Automatic table organization with intelligent micro-partitioning • Clustering keys for data organization • Automatic partition pruning optimization • Supports time-based and custom partitioning strategies | Partitioning by date/time and custom partitions with MergeTree indexes. |
| Result cache | Yes, with cross-user result caching and intelligent cache management for up to 24 hours | Yes, results cache with TTL and query condition cache. |
| Warm cache (SSD) | BI Engine provides in-memory caching and acceleration for frequently accessed data and dashboards | Yes, at indexed data-range level granularity |
| Support for semi-structured data & JSON functions within SQL | Yes, including advanced JSON functions, Lambda expressions, and native support for nested and repeated fields | Yes, including Lambda expressions and native JSON data type (GA in v25.3) |
| Vector Search and AI Capabilities | • BigQuery ML for in-database machine learning • Vertex AI integration • Natural language querying with Gemini AI • Limited vector search capabilities | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference |
| Query Optimizations | • Advanced query optimizer with Dremel engine • Search indexes with column-level granularity • Materialized views with smart refresh and automatic query rewriting • BI Engine in-memory acceleration • Gemini AI-powered query optimization and natural language querying • Cross-user result caching • Automatic partitioning and clustering optimization • Cost-based optimization with intelligent workload management | • Primary indexes (ORDER BY) • Data skipping indexes (minmax, set, bloom filters, ngrambf_v1, tokenbf_v1) • Materialized views • Projections • PREWHERE optimization • Query analysis tools • Automatic global join reordering (v25.9) • Enhanced JSON query optimization • Streaming secondary indices |
BigQuery lines up in benchmarks in the same ballpark as other cloud data warehouses but does come in consistently last in most queries. Beyond implementing according to best practices, there is little you can do to accelerate BigQuery performance, as it determines the amount of resources (slots) the query needs for you. BigQuery can be used together with "BigQuery BI Engine" for lower latency analytics. However, BI Engine is limited in terms of scale because it runs in memory. Its maximum capacity is 100GB.
ClickHouse is famous for being one of the fastest local runtimes ever built for OLAP workloads. Its columnar storage, compression and indexing capabilities make it a consistent leader in benchmarks. Its lack of support for standard SQL and lack of query optimizer means that it's less suitable for traditional BI workloads, and more suitable for engineering managed workloads. While fast, it requires a lot of tuning and optimization.
## Use cases
There are a host of different analytics use cases that can be supported by a data warehouse. Look at your legacy technologies and their workloads, as well as the new possible use cases, and figure out which ones you will need to support in the next few years.
| Feature | BigQuery | ClickHouse |
|---|---|---|
| Low-latency dashboards | • Sub-second to seconds response times at TB+ scale with BI Engine acceleration • Search indexes and materialized views provide significant performance improvements for dashboard queries • Intelligent caching reduces query costs for repeated dashboard access • Dynamic concurrency management supports high user loads | • Sub-second load times at TB+ scale with proper indexing • ClickHouse Cloud reduces engineering overhead with managed service • Proven low-latency performance (120ms at 2500 QPS in benchmarks) • Purpose-built for low-latency OLAP and real-time analytics |
| Enterprise BI | • Mature and comprehensive Enterprise DW feature set with native Google Cloud ecosystem integration • Strong integration with Looker, Looker Studio, and major BI tools • Gemini AI integration for natural language insights • Cross-cloud analytics capabilities • Advanced governance with Dataplex Universal Catalog | • Growing ecosystem with 50+ integrations including major BI tools • Native MySQL protocol support enables broad BI tool compatibility • Strong SQL compliance with PostgreSQL compatibility • Best suited for modern analytical workloads and engineering-managed use cases |
| Data Apps and AI Applications (Customer-facing low-latency high concurrency) | • Dynamic concurrency supporting 1,000+ interactive queries with intelligent queuing and fair scheduling • Sub-second response times with BI Engine acceleration and search indexes • Serverless architecture eliminates infrastructure management overhead • Advanced caching and materialized views optimize repeated queries • AI-powered optimization for customer-facing applications • BigQuery ML for AI workloads | • Sub-second response times at TB+ scale • Supports 1000 concurrent users per replica • Strong price-performance on customer-facing applications • Native vector search and embeddings |
| Ad hoc | • Excellent for ad-hoc analytics with serverless architecture requiring zero infrastructure management • Intelligent query optimization handles unpredictable workloads automatically • Gemini AI integration enables natural language querying for business users • Advanced JSON support and schema inference enable flexible data exploration • Cross-cloud analytics capabilities for federated queries | • Good for ad-hoc queries with ClickHouse Cloud’s separated storage/compute architecture • Join optimizations enable more query complexity • Strong sampling capabilities (TABLESAMPLE) for exploratory analysis • Resource management through user quotas prevents query interference • Materialized views offer performance improvements for common aggregation patterns, ad-hoc users specify directly in SQL |
BigQuery is a mature general-purpose data warehouse, which lends itself well to internal BI & reporting. The fact that it's serverless in nature and tightly integrated with GCP, makes it very convenient for Ad-Hoc analytics and ML use cases on GCP. On the other hand, because BigQuery makes resource allocation decisions for you, it is not always the best fit for operational use cases and Data Apps where performance needs to be consistent and predictable.
ClickHouse was not designed to be a data warehouse, but rather a low-latency query execution runtime. Managing it typically requires significant engineering overhead. Hence, it's a good fit for engineering managed operational use cases and customer-facing data apps, where low latency matters. It is not a good fit for a general purpose data warehouse, nor for Ad-Hoc analytics or ELT.