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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 | Databricks | Snowflake | Firebolt |
|---|---|---|---|
| Separation of storage and compute | Yes | Yes | Yes, separation of storage and metadata as well as compute from compute with full workload isolation. |
| Supported cloud infrastructure | AWS, Azure, GCP. Marketplaces and BYOC | AWS, Azure, GCP with full feature parity across all three major clouds | AWS (GCP coming soon) & anywhere (Firebolt Core) |
| Isolated tenancy – option for dedicated resources | • Control plane in Databricks account • Data plane in customer VPC (optional) • Storage in customer VPC • Serverless SQL runs in Databricks account with private connectivity | • Multi-tenant pooled resources • Isolated tenancy available via VPS tier | • Multi-tenant metadata layer • Isolated tenancy for compute & storage per client |
| Control vs abstraction of compute | • 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 | • Configurable warehouse sizes (XS to 6XL) • Multi-cluster warehouses with auto-scaling • Choice between Generation 1 and Generation 2 standard warehouses • MAX_CONCURRENCY_LEVEL parameter for resource allocation | Uses engine abstraction: • Each engine has configurable cluster size (1-128 nodes) for horizontal scaling. • Configurable compute family (compute vs storage optimized) and type (XS, S, M, L, XL) for vertical scaling • Number of clusters for concurrency (auto)scaling. Provides full workload isolation across engines. |
| Self-hosted and hybrid deployment options | • Databricks on customer cloud accounts • Unity Catalog for hybrid governance | Snowflake for Government Cloud and private cloud options available | • Firebolt Core: Forever free, self-hosted edition with full query engine capabilities • Same performance and features as managed service • Deploy anywhere: local laptop, cloud, datacenter, Kubernetes • Production-grade distributed architecture • No usage restrictions except building competing SaaS |
| ACID Compliance and Transactions | • ACID transactions with Delta Lake • Time travel and versioning • Concurrent read/write operations | Full ACID compliance with Time Travel and zero-copy cloning capabilities | • Full ACID compliance with snapshot isolation • Multi-statement transactions supported • Strong consistency across all operations • Supports concurrent reads and writes • Transactional integrity for data applications |
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.
Snowflake was one of the first decoupled storage and compute architectures, making it the first to have nearly unlimited compute scale and workload isolation, and horizontal user scalability. It runs on AWS, Azure and GCP. It is multi-tenant over shared resources in nature and requires you to move data out of your VPC and into the Snowflake cloud. “Virtual Private Snowflake” (VPS) is its highest-priced tier, and can run a dedicated isolated version of Snowflake. Its virtual warehouses can be T-shirt sized along an XS/S/M…/4XL axis, where each discrete T-shirt size is bundled with fixed HW properties that are abstracted from the users. Snowflake has recently added support for Snowflake managed Iceberg tables.
Firebolt is built on a natively decoupled storage & compute architecture, on AWS only. Data has to be copied outside of your VPC into the Firebolt, where both your compute and data run in a dedicated and isolated tenant. A “Firebolt Engine” can be granularly configured across # of nodes and different CPU/RAM/SSD combinations.
## 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 | Databricks | Snowflake | Firebolt |
|---|---|---|---|
| Elasticity – Scaling for larger data volumes and faster queries | Autoscaling clusters based on workload demand. Serverless SQL warehouses provide near-instant scaling (2-6 seconds startup) | • Instant warehouse resize (XS to 6XL) with no downtime • Multi-cluster auto-scaling • Generation 2 warehouses provide ~2x performance improvement over Generation 1 | Granular cluster resize with node types, number of nodes and number of clusters. Zero downtime. |
| Elasticity – Scaling for higher concurrency | • 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 | • Single warehouse supports many concurrent queries (MAX_CONCURRENCY_LEVEL=8 controls resource allocation per query, not query limit) • Multi-cluster warehouses enable thousands of concurrent queries with auto-scaling • Unlimited virtual warehouses can be created | A single engine can handle hundreds of concurrent queries. Engines auto-scale the number of clusters up and down base on resource usage thresholds. Idle engines scale down to zero billing. |
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.
Snowflake scales very well both for data volumes and query concurrency. The decoupled storage/compute architecture supports resizing clusters without downtime, and in addition, supports auto-scaling horizontally for higher query concurrency during peak hours.
Firebolt can handle the largest data volumes and concurrency on a single comparable cluster size, thanks to its superior hardware efficiency. Thanks to its decoupled storage & compute architecture it scales very well to large data volumes. However, resizing an engine size isn’t instant and requires orchestration if avoiding downtime is necessary. A single Firebolt engine can support hundreds of concurrent queries, avoiding the need to scale out for most use cases. Scaling horizontally for even higher concurrency is manual.
## 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 | Databricks | Snowflake | Firebolt |
|---|---|---|---|
| Indexes | None | • Search Optimization Service for point lookups and selective queries (additional cost) • Clustering keys for data organization and automatic clustering • Materialized views • Snowflake Optima automatic indexing on Generation 2 warehouses (no additional cost) • No traditional database indexes | • Sparse primary indexes • Aggregating indexes • Join indexes • Optimizer driven index usage |
| Compute tuning | Choice of cluster type, node types including SSD-optimized instances. Serverless provides automatic resource allocation with Intelligent Workload Management (IWM) | • Warehouse T-shirt sizing (XS to 6XL) • Multi-cluster configuration and scaling policies • Generation 1 vs Generation 2 warehouse selection • MAX_CONCURRENCY_LEVEL parameter tuning • Query Acceleration Service for long-running queries | SQL defined engines. Control number of nodes, node family and type per cluster, with one or more clusters per engine. Multiple engines isolate workloads. |
| Storage format | • 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 | Columnar micro-partitioned & compressed storage | Columnar, sorted & compressed & sparsely indexed storage (F3 – Firebolt File Format) with native Apache Iceberg support |
| Table-level partition & pruning techniques | • 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 | • Data automatically divided into micro-partitions • Automatic pruning at micro-partition level • Clustering keys for data organization with automatic clustering • Snowflake Optima provides additional automatic pruning optimization on Gen2 warehouses | • User-defined table-level partitions are optional. • Data is automatically sorted, compressed and indexed into F3 format. • Pruning at indexed data-range level. |
| Result cache | Multi-layered caching: local in-memory cache per cluster plus remote result cache (serverless only) that persists across all warehouses in workspace | Yes | Yes, results and sub-results cache with transactional spoiling. |
| Warm cache (SSD) | Yes. Delta cache for data read by queries at file level granularity | Yes, at micro-partition level granularity | Yes, at indexed data-range level granularity |
| Support for semi-structured data & JSON functions within SQL | Yes | Yes | Yes, including Lambda expressions and native nested array structures |
| Vector Search and AI Capabilities | • MLflow integration and Databricks ML platform • Native vector search in Delta Lake (Vector Search) • AI and ML workloads optimized | AI integration through Cortex AI and Snowpark ML | • Native vector search capabilities and embeddings • MCP Server for AI driven analytics • Natural Language to SQL • SQL based Inference |
| Query Optimizations | • 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 | • Search Optimization Service for point lookups (additional cost) • Query Acceleration Service (QAS) for long-running and unpredictable workloads • Snowflake Optima automatic optimization on Generation 2 warehouses (no additional cost) • Automatic clustering with background maintenance • Materialized views with automatic refresh • Result cache (24hrs) • Cost-based optimization with dynamic query rewriting | • Primary indexes, aggregating indexes, join indexes, sparse indexes • Sub-plan result caching • F3 storage format optimization • Automatic query optimizer with aggressive pruning • Late column materialization • Query analysis tools based on execution telemetry |
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.
Snowflake typically comes on top for most queries when it comes to performance in public TPC-based benchmarks when compared to BigQuery and Redshift, but only marginally. Its micro partition storage approach effectively scans less data compared to larger partitions. The ability to isolate workloads over the decoupled storage & compute architecture lets you avoid competition for resources compared to multi-tenant shared resource solutions, and the ability to increase warehouse sizes can often enhance performance (for a higher price), but not always linearly. Snowflake’s recently released “Search optimization service” delivers index-like behavior for point queries, but comes at an additional cost.
Firebolt is the fastest when it comes to query performance when compared to cloud data warehouses and services like Athena. Its unique approach to storage and indexing results in highly aggressive data pruning that scans dramatically less data compared to other technologies. While other technologies scan partitions or micro-partitions, Firebolt works with indexed data ranges that are significantly smaller. In addition, Firebolt lets users accelerate queries further with multiple index types (Aggregating index, Join index), and using its decoupled storage & compute architecture workloads can be easily isolated to guarantee consistent performance.
## 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 | Databricks | Snowflake | Firebolt |
|---|---|---|---|
| Low-latency dashboards | • 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 | • Sub-second to seconds response times at TB+ scale with proper clustering and optimization • Enhanced by Query Acceleration Service and Search Optimization Service • Generation 2 warehouses provide ~2x performance improvement over Generation 1 • Snowflake Optima provides automatic optimization | • 120ms query latency at 4000 QPS (FireScale benchmark 2025) • Sub-second performance at TB+ scale with proper indexing • Built for AI-driven analytics, dashboards, and real-time analytic applications |
| Enterprise BI | • Strong for data science and ML workloads • Unified analytics platform approach • Growing traditional BI integrations • Serverless SQL warehouses improve accessibility • Delta sharing capabilities | • Mature and comprehensive Enterprise DW feature set • Extensive integrations with Enterprise BI ecosystem • Multi-cloud deployment options with consistent experience • Strong SQL compliance and wide ecosystem support • Zero-copy data sharing capabilities | • Growing ecosystem with focus on modern BI tools • Strong SQL compliance with PostgreSQL • Wire level compatibility drives expansion to PostgreSQL BI and ETL ecosystem |
| Data Apps and AI Applications (Customer-facing low-latency high concurrency) | • 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 | • Multi-cluster warehouses support thousands of concurrent users with auto-scaling • Individual warehouses support many concurrent queries (not limited to 8 concurrent queries) • Sub-second to seconds response times with proper optimization • Generation 2 warehouses provide significant performance improvements for high-concurrency workloads • AI integration through Cortex AI | • 120ms latency at 4000+ QPS proven performance at TB+ scale • Supports hundreds to thousands of concurrent queries on single engine • Price-performance leader (8x better than Snowflake, 18x vs Redshift) • Purpose-built for AI agents and data-intensive applications • Native vector search and embeddings |
| Ad hoc | • 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 | • Excellent for ad-hoc with decoupled storage/compute • Auto-scaling and instant compute provisioning • Minimal predefined optimization required • Query Acceleration Service handles unpredictable workloads automatically • Snowflake Optima provides automatic optimization for recurring patterns | • Excellent performance out-of-the-box with engine optimized for star and snowflake joins and aggregations • Self learning query plan optimizer • Full workload isolation prevents ad-hoc complexity from affecting real-time workloads • Aggregating indexes are automatically used by optimizer |
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.
Snowflake is a well rounded general purpose cloud data warehouse, that can also span beyond traditional BI & Analytics use cases into Ad-Hoc and ML use cases. Thanks to the flexible decoupeld storage & compute architecture that allows you to isolate and control the amount of compute per workload, it’s possible to tackle a broad spectrum of workloads. However, like its close siblings Redshift & BigQuery, it struggles to deliver low-latency query performance at scale, making it a lesser fit for operational use cases and customer-facing data apps.
Firebolt stands out by being the fastest cloud data warehouse when compared to Snowflake, Redshift, BigQuery and Athena. It’s great for delivering sub-second analytics at scale, while remaining hardware efficient and high concurrency friendly. This makes it a great choice for operational use cases and customer-facing data apps. Given that it is not as feature-rich and integration rich as the more mature data warehouses makes it a lesser fit for a general-purpose Enterprise data warehouse. It is also not the best fit for ad-hoc use cases, because of the need to predefine indexing at the table level.