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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 | Redshift |
|---|---|---|
| Separation of storage and compute | Fully serverless with complete separation of compute (Dremel) and storage (Colossus), powered by Jupiter network and Borg orchestration | RA3 instances enable separation of compute and storage, but limited workload isolation compared to other platforms |
| 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 | • Isolated tenant & resources • Runs in your VPC |
| 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 • Configurable compute types |
| Self-hosted and hybrid deployment options | No self-hosted options – fully managed service only | Limited hybrid options with Redshift Serverless |
| ACID Compliance and Transactions | Limited ACID support – eventual consistency model with some transactional capabilities | ACID compliant at table level with some limitations on concurrent 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.
Redshift has the oldest architecture, being the first Cloud DW in the group. Its architecture wasn't designed to separate storage & compute. While it now has RA3 nodes which allow you to scale compute and only cache the data you need locally, all compute still operates together. You cannot separate and isolate different workloads over the same data, which puts it behind other decoupled storage/compute architectures. Redshift runs as an isolated tenant per customer, and unlike other cloud data warehouses, it is deployed in your VPC. Redshift offers a serverless option which is based on an abstracted unit called Redshift Processing Unit (RPU) ranging from 8 to 512 in increments of 8. Each RPU provides 2 vCPU and 16GB RAM. Thus, 8 RPU is equivalent to 16 vCPU / 128GB RAM. The minimum RPU is 8.
## 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 | Redshift |
|---|---|---|
| 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 | Available via Elastic Resize – slow and limited, downtime required |
| 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 | • 5 concurrent queries per WLM queue by default (up to 8 queues) • Concurrency Scaling enables thousands of concurrent queries |
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.
Redshift is limited in scale because even with RA3, it cannot distribute different workloads across clusters. While it can scale to up to 10 clusters automatically to support query concurrency, it can only handle a maximum of 50 queued queries across all clusters by default.
## 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 | Redshift |
|---|---|---|
| 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 over number of nodes and their type |
| 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 & compressed storage (RA3 nodes) |
| 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 | • No table partitions • User-defined distribution & sort keys are used to optimize for speed |
| Result cache | Yes, with cross-user result caching and intelligent cache management for up to 24 hours | Yes |
| Warm cache (SSD) | BI Engine provides in-memory caching and acceleration for frequently accessed data and dashboards | Only with RA3 nodes at partition-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 | Limited |
| 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 | Limited AI capabilities – primarily through integrations |
| 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 | • Basic query optimizer • Materialized views • Result caching • ANALYZE for table statistics • Workload management (WLM) • Automated materialized views (AutoMV) • AI-driven scaling (Serverless preview) |
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.
Redshift does provide a result cache for accelerating repetitive query workloads and also has more tuning options than some others. But it does not deliver much faster compute performance than other cloud data warehouses in benchmarks. Sort keys can be used to optimize performance, but their contribution is limited. There is no support for indexes, and low-latency analytics at large data volumes is hard to achieve. Because Redshift decoupling of storage & compute is limited compared to other cloud data warehouses, it doesn't support isolating workloads, which means performance can degrade under pressure and competition for resources.
## 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 | Redshift |
|---|---|---|
| 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 tens of seconds load times at 100s of GB scale • Can achieve faster performance with Concurrency Scaling and proper tuning |
| 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 | • Mature and comprehensive Enterprise DW feature set • Extensive integrations with Enterprise BI ecosystem • Strong AWS ecosystem integration |
| 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 | • 5 concurrent queries per WLM queue by default (up to 8 queues) • Concurrency Scaling enables thousands of concurrent queries • Seconds-level response times typical • Automatic scaling for burst workloads • Limited AI application support |
| 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 | • Performance dependent on predefined distribution & sort keys • Elastic Resize enables adding compute resources • Typically subset of data loaded for ad-hoc analysis |
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.
Redshift was originally designed to support traditional internal BI reporting and dashboard use cases for analysts. As such, it is typically used as a general-purpose Enterprise data warehouse. With deep integrations into the AWS ecosystem, it can also leverage AWS ML service, making it also useful for ML projects. However, given the coupling of storage & compute, and the difficulty in delivering low-latency analytics at scale, it is less suited for operational use cases and customer-facing use cases like Data Apps. The coupling of storage and compute, together with the need to predefine sort & dist keys for optimal performance, make it challenging to use for Ad-Hoc analytics.