Compare data warehouse services (Redshift, Synapse, BigQuery) across providers.
Last verified: May 2026
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| Service | Amazon Redshift | Azure Synapse Analytics | BigQuery |
| Pricing model | Per-node hour (on-demand or reserved instances) | DWU-hours (Dedicated SQL pool) or pay-per-query (Serverless) | Per-query TB scanned (on-demand) or flat-rate slots |
| Serverless option | Redshift Serverless (auto-scales RPU capacity) | Serverless SQL pool (pay-per-query over data lake) | Default serverless (no infrastructure to manage) |
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| Storage format | Columnar (custom compressed columnar format) | Columnar (clustered columnstore indexes) | Columnar (Capacitor format with automatic optimization) |
| Max storage | Unlimited with Redshift Spectrum (query S3 data lake directly) | Unlimited (external tables via data lake integration) | Unlimited (automatic storage scaling, no configuration needed) |
| Semi-structured data | SUPER type (semi-structured data with PartiQL queries) | JSON functions and OPENJSON for parsing JSON data | Native nested and repeated fields (STRUCT and ARRAY types) |
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| Concurrency | 50+ concurrent queries (with concurrency scaling up to thousands) | Depends on DWU level (higher DWU = more concurrent queries) | Up to 2,000 concurrent queries (automatic slot scheduling) |
| Materialized views | Supported with auto-refresh and incremental maintenance | Supported with automatic maintenance in dedicated SQL pool | Supported with smart tuning and auto-refresh capabilities |
| Query caching | Result caching enabled by default (up to 24 hours) | Result-set caching in dedicated SQL pool (up to 48 hours) | Automatic query caching (cached results are free, 24-hour TTL) |
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| ML integration | Redshift ML (create models with SQL using SageMaker Autopilot) | Synapse ML (integrated notebooks, Spark, and Azure ML) | BigQuery ML (train and predict with SQL, supports TensorFlow) |
| Streaming ingestion | Amazon Kinesis Data Streams and Kinesis Data Firehose | Azure Event Hubs and Stream Analytics integration | Pub/Sub direct streaming inserts via Storage Write API |
| Data sharing | Redshift Data Sharing (cross-cluster and cross-account live sharing) | Synapse Link (live analytics over operational data stores) | Analytics Hub (data exchange with shared datasets and listings) |
| BI integration | Native Amazon QuickSight; JDBC/ODBC for Tableau, Looker, Power BI | Native Power BI integration; JDBC/ODBC for Tableau, Looker | Native Looker and Looker Studio; JDBC/ODBC for Tableau, Power BI |
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| Geospatial | Limited GEOMETRY and GEOGRAPHY types (basic spatial functions) | PostGIS-compatible spatial data types and functions | Full GEOGRAPHY type with extensive GIS functions (ST_ functions) |
| Feature | AWS | Azure | GCP |
|---|---|---|---|
| Cost estimate | Starts ~$0.25/hr (dc2.large on-demand); Serverless from $0.375/RPU-hr | ~$1.20/hr for 100 DWU; scales linearly with DWU level | $6.25 per TB scanned (on-demand); flat-rate from $2,000/mo for 100 slots |
[
{
"feature": "Service",
"category": "General",
"aws": "Amazon Redshift",
"azure": "Azure Synapse Analytics",
"gcp": "BigQuery"
},
{
"feature": "Pricing model",
"category": "General",
"aws": "Per-node hour (on-demand or reserved instances)",
"azure": "DWU-hours (Dedicated SQL pool) or pay-per-query (Serverless)",
"gcp": "Per-query TB scanned (on-demand) or flat-rate slots"
},
{
"feature": "Serverless option",
"category": "General",
"aws": "Redshift Serverless (auto-scales RPU capacity)",
"azure": "Serverless SQL pool (pay-per-query over data lake)",
"gcp": "Default serverless (no infrastructure to manage)"
},
{
"feature": "Storage format",
"category": "Storage & Format",
"aws": "Columnar (custom compressed columnar format)",
"azure": "Columnar (clustered columnstore indexes)",
"gcp": "Columnar (Capacitor format with automatic optimization)"
},
{
"feature": "Max storage",
"category": "Storage & Format",
"aws": "Unlimited with Redshift Spectrum (query S3 data lake directly)",
"azure": "Unlimited (external tables via data lake integration)",
"gcp": "Unlimited (automatic storage scaling, no configuration needed)"
},
{
"feature": "Semi-structured data",
"category": "Storage & Format",
"aws": "SUPER type (semi-structured data with PartiQL queries)",
"azure": "JSON functions and OPENJSON for parsing JSON data",
"gcp": "Native nested and repeated fields (STRUCT and ARRAY types)"
},
{
"feature": "Concurrency",
"category": "Performance",
"aws": "50+ concurrent queries (with concurrency scaling up to thousands)",
"azure": "Depends on DWU level (higher DWU = more concurrent queries)",
"gcp": "Up to 2,000 concurrent queries (automatic slot scheduling)"
},
{
"feature": "Materialized views",
"category": "Performance",
"aws": "Supported with auto-refresh and incremental maintenance",
"azure": "Supported with automatic maintenance in dedicated SQL pool",
"gcp": "Supported with smart tuning and auto-refresh capabilities"
},
{
"feature": "Query caching",
"category": "Performance",
"aws": "Result caching enabled by default (up to 24 hours)",
"azure": "Result-set caching in dedicated SQL pool (up to 48 hours)",
"gcp": "Automatic query caching (cached results are free, 24-hour TTL)"
},
{
"feature": "ML integration",
"category": "Integration",
"aws": "Redshift ML (create models with SQL using SageMaker Autopilot)",
"azure": "Synapse ML (integrated notebooks, Spark, and Azure ML)",
"gcp": "BigQuery ML (train and predict with SQL, supports TensorFlow)"
},
{
"feature": "Streaming ingestion",
"category": "Integration",
"aws": "Amazon Kinesis Data Streams and Kinesis Data Firehose",
"azure": "Azure Event Hubs and Stream Analytics integration",
"gcp": "Pub/Sub direct streaming inserts via Storage Write API"
},
{
"feature": "Data sharing",
"category": "Integration",
"aws": "Redshift Data Sharing (cross-cluster and cross-account live sharing)",
"azure": "Synapse Link (live analytics over operational data stores)",
"gcp": "Analytics Hub (data exchange with shared datasets and listings)"
},
{
"feature": "BI integration",
"category": "Integration",
"aws": "Native Amazon QuickSight; JDBC/ODBC for Tableau, Looker, Power BI",
"azure": "Native Power BI integration; JDBC/ODBC for Tableau, Looker",
"gcp": "Native Looker and Looker Studio; JDBC/ODBC for Tableau, Power BI"
},
{
"feature": "Geospatial",
"category": "Advanced",
"aws": "Limited GEOMETRY and GEOGRAPHY types (basic spatial functions)",
"azure": "PostGIS-compatible spatial data types and functions",
"gcp": "Full GEOGRAPHY type with extensive GIS functions (ST_ functions)"
},
{
"feature": "Cost estimate",
"category": "Pricing",
"aws": "Starts ~$0.25/hr (dc2.large on-demand); Serverless from $0.375/RPU-hr",
"azure": "~$1.20/hr for 100 DWU; scales linearly with DWU level",
"gcp": "$6.25 per TB scanned (on-demand); flat-rate from $2,000/mo for 100 slots"
}
]The Multi-Cloud Data Warehouse Compare tool provides a side-by-side evaluation of managed data warehouse services including Amazon Redshift, Azure Synapse Analytics, and Google BigQuery. It compares architecture approaches, pricing models, query performance characteristics, concurrency handling, and ecosystem integrations. The comparison covers both provisioned and serverless options for each provider, helping analytics teams and data engineers choose the warehouse that best fits their query volume, data size, and budget. It also highlights differentiators like BigQuery's slot-based pricing, Redshift Spectrum's external table support, and Synapse's unified analytics workspace.
Your team is replacing a 5-year-old on-prem Teradata warehouse with 50 TB of data. Workload analysis shows: 200 BI users running 10,000 queries/day (mostly small/medium scans), plus 50 nightly ETL jobs scanning the full warehouse. Compare tool shows: BigQuery on-demand = $4,500/month for query scans + $1,000 storage, with autoscaling concurrency for free; Redshift RA3 with reservations = $6,200/month with predictable capacity; Synapse Dedicated DW1500c = $7,800/month. BigQuery wins on price AND on the ad-hoc concurrency story. Migration green-lit.
BigQuery's per-TB-scanned model wins for unpredictable, ad-hoc analytics workloads. Redshift Provisioned wins for predictable, high-concurrency dashboards. Synapse wins when you're already deep in the Azure ecosystem (Power BI, Synapse Studio, Azure Data Factory tight integration). Cost alone rarely decides — workload pattern matters more.
All three offer separation of compute and storage now (BigQuery natively, Redshift via RA3, Synapse via Hyperscale tier). This is the right architecture for almost everyone in 2026 — old node-based Redshift configs that bundle compute+storage are legacy.
Redshift Spectrum and Synapse serverless SQL both let you query data lake files without loading. Both charge $5/TB scanned. BigQuery external tables are similar but BigLake adds row-level/column-level security on top. For governed data lakes, BigLake is the strongest of the three.
The compare tool surfaces 25+ feature dimensions across the three data warehouses: pricing model (per-TB, per-slot-hour, per-DWU-hour), separation of compute/storage, max single-query data scan, concurrency (concurrent queries supported), serverless option availability, ML/AI integration (BigQuery ML, Redshift ML, Synapse ML), external table/data lake querying support, and ecosystem integrations (Looker, Power BI, Tableau, etc.).
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