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## 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 | Databricks |
|---|---|---|
| Separation of storage and compute | Fully serverless with complete separation of compute (Dremel) and storage (Colossus), powered by Jupiter network and Borg orchestration | Yes |
| Supported cloud infrastructure | Google Cloud only | AWS, Azure, GCP. Marketplaces and BYOC |
| 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 | • Control plane in Databricks account • Data plane in customer VPC (optional) • Storage in customer VPC • Serverless SQL runs in Databricks account with private connectivity |
| 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 clusters and instance types • Serverless SQL warehouses (GA 2025) run in Databricks account with private connectivity, no public IPs • Pro/Classic warehouses run in customer VPC |
| Self-hosted and hybrid deployment options | No self-hosted options – fully managed service only | • Databricks on customer cloud accounts • Unity Catalog for hybrid governance |
| ACID Compliance and Transactions | Limited ACID support – eventual consistency model with some transactional capabilities | • ACID transactions with Delta Lake • Time travel and versioning • Concurrent read/write operations |
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.
Databricks was built by the founders of Spark as an analytics platform to support machine learning use cases. It leverages the Spark framework to process data residing in a data lake and is supported on AWS, GCP and Azure. Databricks coined the marketing term “Lakehouse '' architecture to illustrate the unification of data lake and data warehouse use cases. Customers still manage Spark clusters that process data residing in a Delta lake. Conversion of data to Delta Lake format is required to leverage the functionality of Delta Lake. Databricks Sql is a relatively new addition to simplify access to data stored in a data lake.
## 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 | Databricks |
|---|---|---|
| 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 | Autoscaling clusters based on workload demand. Serverless SQL warehouses provide near-instant scaling (2-6 seconds startup) |
| 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 | • 10 concurrent queries per cluster limit • Scales up to 40 clusters per warehouse (400 total concurrent queries) • Serverless SQL warehouses provide near-instant autoscaling • Pro/Classic warehouses take several minutes to provision new clusters • Real-world performance degradation typically occurs at 50-150 concurrent queries depending on complexity |
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 advance. With reserved slots there is more control over compute resources, thus making scaling more predictable. Concurrency is limited to 100 users by default.
Databricks allow for autoscaling of clusters based on utilization. Additionally, increasing concurrency associated with a sql endpoint can be accomplished through the addition of clusters. Query concurrency per cluster is maxed at 10. However, scaling with additional clusters for concurrency is possible. Databricks provides a choice of instance types.
## 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 | Databricks |
|---|---|---|
| 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 | None |
| Compute tuning | Serverless architecture with automatic resource optimization – no manual tuning required. Intelligent workload management with AI-powered resource allocation and dynamic slot distribution | Choice of cluster type, node types including SSD-optimized instances. Serverless provides automatic resource allocation with Intelligent Workload Management (IWM) |
| 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 | • Delta Lake format with Liquid Clustering (February 2025 – replaces Z-ordering and traditional partitioning) • Cannot use Liquid Clustering alongside Z-ordering on same table • Allows for sorted data in Delta Lake • Requires Optimize to maintain ordering |
| 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 | • Table level partitioning • Liquid Clustering for improved query performance and reduced data skew (February 2025) • Z-ordering (legacy, replaced by Liquid Clustering) • Periodic optimization of storage required |
| Result cache | Yes, with cross-user result caching and intelligent cache management for up to 24 hours | Multi-layered caching: local in-memory cache per cluster plus remote result cache (serverless only) that persists across all warehouses in workspace |
| Warm cache (SSD) | BI Engine provides in-memory caching and acceleration for frequently accessed data and dashboards | Yes. Delta cache for data read by queries at file 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 |
| 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 | • MLflow integration and Databricks ML platform • Native vector search in Delta Lake (Vector Search) • AI and ML workloads optimized |
| 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 | • Photon engine (C++ vectorized engine providing 3-8x average speedups, maximum speedups over 10x) • Automated stats collection (January 2025) enables cost-based optimization • Predictive I/O for faster point lookups and data updates • Liquid Clustering (February 2025) • Intelligent Workload Management (IWM) with AI-powered resource allocation • Delta cache • Materialized views support |
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 the “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.
Databricks is designed to leverage the Spark framework for processing large volumes of data. It leverages compressed Parquet files in a Delta Lake. To reduce the amount of data processed, it uses data pruning on partitions and Parquet file metadata. Databricks does not provide any indexes.
## 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 | Databricks |
|---|---|---|
| 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 to seconds load times at TB+ scale • Enhanced by Photon engine (3-8x average speedups) and Delta cache • Serverless SQL warehouses provide rapid startup (2-6 seconds) • Performance depends on cluster configuration |
| 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 | • Strong for data science and ML workloads • Unified analytics platform approach • Growing traditional BI integrations • Serverless SQL warehouses improve accessibility • Delta sharing capabilities |
| 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 | • 10 concurrent queries per cluster, scaling to 400 total concurrent queries per warehouse • Real-world performance degradation typically occurs at 50-150 concurrent queries depending on workload complexity • Serverless provides near-instant autoscaling • Photon engine delivers 3-8x performance improvements • Strong ML and AI platform integration |
| 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 | • Excellent for ad-hoc with decoupled storage/compute • Serverless SQL warehouses provide instant provisioning • Intelligent Workload Management handles unpredictable workloads automatically • Strong for exploratory data analysis and ML workloads • Automated stats collection improves query planning |
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.
Databricks is a mature Spark based platform proven for processing streaming data. It is widely used for Machine Learning use cases by data scientists through the use of integrated notebooks. From a low latency query perspective, while it offers features like Delta Cache, it does not provide specialized indexes that can deliver low latency queries.