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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 | Athena |
|---|---|---|
| Separation of storage and compute | Fully serverless with complete separation of compute (Dremel) and storage (Colossus), powered by Jupiter network and Borg orchestration | Yes, serverless with optional provisioned capacity. Workloads can be isolated through Workgroups and Capacity Reservations |
| Supported cloud infrastructure | Google Cloud only | AWS only |
| 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 pooled resources by default • Dedicated compute resources available via Provisioned Capacity • VPC endpoint connections supported |
| 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 | • 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 | No self-hosted options – fully managed service only | No self-hosted options – serverless only |
| ACID Compliance and Transactions | Limited ACID support – eventual consistency model with some transactional capabilities | No ACID compliance – eventual consistency model |
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.
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
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 | Athena |
|---|---|---|
| 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 | • 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 | 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 | • 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 |
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.
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 time outs. Query concurrency is maxed at 20. If scalability is a top priority, Athena is probably not the best choice.
## 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 | Athena |
|---|---|---|
| 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 | No traditional indexes – relies on partition pruning and data organization in S3. Uses columnar formats and compression for optimization |
| Compute tuning | Serverless architecture with automatic resource optimization – no manual tuning required. Intelligent workload management with AI-powered resource allocation and dynamic slot distribution | • 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 | 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 | 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 | • Automatic table organization with intelligent micro-partitioning • Clustering keys for data organization • Automatic partition pruning optimization • Supports time-based and custom partitioning strategies | • 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 | Yes, with cross-user result caching and intelligent cache management for up to 24 hours | Query result caching for up to 30 days with configurable retention. Results reuse supported across workgroups |
| Warm cache (SSD) | BI Engine provides in-memory caching and acceleration for frequently accessed data and dashboards | 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, including advanced JSON functions, Lambda expressions, and native support for nested and repeated fields | Yes, comprehensive JSON support including Lambda expressions, array functions, and native nested data handling |
| 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 | No native AI or vector search capabilities |
| 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 | • 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 |
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.
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 be more performant better than Athena in most 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 | BigQuery | Athena |
|---|---|---|
| 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 | • 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 | • 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 | • 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) | • 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 | • 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 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 | • 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 |
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.
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.