# Databricks vs Athena (/comparison/databricks-vs-athena)
## Architecture [#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 | Athena |
| ------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Separation of storage and compute | Yes | Yes, serverless with optional provisioned capacity. Workloads can be isolated through Workgroups and Capacity Reservations |
| Supported cloud infrastructure | AWS, Azure, GCP. Marketplaces and BYOC | AWS only |
| 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 by default
• Dedicated compute resources available via Provisioned Capacity
• VPC endpoint connections supported |
| 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 | • Serverless by default with no infrastructure control
• Optional Provisioned Capacity allows dedicated DPU allocation (minimum 24 DPUs)
• Two pricing models: on-demand ($5/TB scanned) or provisioned ($0.30/DPU-hour) |
| Self-hosted and hybrid deployment options | • Databricks on customer cloud accounts
• Unity Catalog for hybrid governance | No self-hosted options – serverless only |
| ACID Compliance and Transactions | • ACID transactions with Delta Lake
• Time travel and versioning
• Concurrent read/write operations | No ACID compliance – eventual consistency model |
**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.
**Athena** is serverless and built on a decoupled storage and compute architecture that queries data directly in S3, without the need to ingest/copy the data. It runs in multi-tenancy with shared resources. Users do not have control over the compute resources Athena chooses to allocate per query from the shared resource pool. For folks requiring additional or dedicated resources, they can reserve dedicated processing capacity in the form of Data Processing Units (DPU), with each DPU providing 4 vCPU and 16 GB RAM. RPU allocation ranges from 24 - 1000 per region.
## Scalability [#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 | Athena |
| --------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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) | • Fully abstracted on-demand scaling • Provisioned Capacity allows manual scaling of DPUs for predictable performance • Capacity reservations can be adjusted with minimum 1-hour billing periods |
| 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 | • Default limit of 25 concurrent DML queries and 20 DDL queries (adjustable via service quotas) • Provisioned Capacity enables higher concurrency with dedicated DPUs • Query queuing available when capacity is exceeded |
\*\*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.
**Athena** is a shared multi-tenant resource, with no guarantees on the amount or availability of the resources allocated for your queries. From a data volume perspective, it can scale to large volumes, but large data volumes can suffer from very long run times and frequent timeouts. Query concurrency is maxed at 20. If scalability is a top priority, Athena is probably not the best choice.
## Performance [#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 | Athena |
| ------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Indexes | None | No traditional indexes – relies on partition pruning and data organization in S3. Uses columnar formats and compression for optimization |
| Compute tuning | Choice of cluster type, node types including SSD-optimized instances. Serverless provides automatic resource allocation with Intelligent Workload Management (IWM) | • No compute tuning in on-demand mode • Provisioned Capacity allows DPU allocation control (4 vCPU and 16GB RAM per DPU) • Minimum 24 DPUs with scaling in 4-DPU increments |
| 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 | Supports multiple formats: Parquet, ORC, Avro, JSON, CSV, TSV on S3. Native support for open table formats including Apache Iceberg, Apache Hudi, and Delta Lake |
| 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 | • User-defined table-level partitions with Hive-style partitioning • Pruning at partition level • Partition projection for advanced performance optimization • Supports open table formats with built-in partitioning |
| Result cache | Multi-layered caching: local in-memory cache per cluster plus remote result cache (serverless only) that persists across all warehouses in workspace | Query result caching for up to 30 days with configurable retention. Results reuse supported across workgroups |
| Warm cache (SSD) | Yes. Delta cache for data read by queries at file level granularity | No local caching – queries data directly from S3. Relies on S3’s performance characteristics and intelligent tiering |
| Support for semi-structured data & JSON functions within SQL | Yes | Yes, comprehensive JSON support including Lambda expressions, array functions, and native nested data handling |
| Vector Search and AI Capabilities | • MLflow integration and Databricks ML platform • Native vector search in Delta Lake (Vector Search) • AI and ML workloads optimized | No native AI or vector search capabilities |
| 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 | • Cost-based optimizer (CBO) in Athena engine v3 • Query result caching (up to 30 days) • Partition projection for advanced optimization • CTAS for precomputed queries • Join reordering and aggregation pushdown • Automatic parallel query execution • Support for columnar formats (Parquet, ORC) • Integration with AWS Glue Data Catalog |
**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.
**Athena** (and Presto) are designed to query data where it is, sacrificing storage-compute optimizations. This makes it very convenient for easy and immediate querying but at the expense of performance. This typically puts Athena behind cloud data warehouses in terms of performance. But Athena still does relatively well in performance benchmarks, especially when external storage is managed by experts. While it supports partitions, there is no support for indexing, and together with the fact that resources are pooled from a shared multi-tenant service, low-latency and consistent performance are not Athena’s sweet spot. A cloud data warehouse is more performant than Athena in most cases.
## Use cases [#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 | Athena |
| ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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 | • Seconds to minutes response times for interactive dashboards • Performance varies based on data partitioning, file formats, and query optimization • Provisioned Capacity can improve consistency for dashboard workloads • Best suited for analytical dashboards rather than sub-second operational dashboards |
| 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 | • Good integration with AWS ecosystem BI tools (QuickSight, etc.) • Standard SQL compatibility enables most BI tool connections • Cost-effective for variable workloads and ad-hoc analytics • JDBC/ODBC drivers support enterprise BI tools • Limited advanced BI features compared to dedicated data warehouses |
| 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 | • Default concurrency limits (25 DML/20 DDL queries) may require service quota increases • Provisioned Capacity enables higher concurrency with dedicated resources • Seconds-level response times typical • Cost-effective for customer-facing analytics with proper optimization • Best suited for analytical rather than operational workloads • No native AI capabilities |
| 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 | • Purpose-built for ad-hoc analytics on data lakes • Serverless with zero infrastructure management • Direct querying of S3 data without ETL • Cost-effective pay-per-query model ideal for exploratory analysis • Strong support for multiple data formats and federated queries • Apache Spark integration for advanced analytics |
\*\*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.
**Athena** is a great choice for Ad-Hoc analytics. You can keep the data where it is, and start querying without worrying about hardware or pretty much anything else, given that Athena is serverless and takes care of everything behind the scenes. However, it is not a great fit when you need consistent and fast query performance, and/or high concurrency. This is why it is typically not the best choice for operational and customer-facing applications. It can be also easily and flexibly used for batch processing, which is often leveraged for ML use cases.