Data Engineering
The wrong data warehouse choice means months of migration pain. This comparison covers Snowflake, BigQuery, Redshift, and Databricks with real Amazon seller workload benchmarks.
Dec 5, 2025 · 7 min read · 1,340 words
Updated October 8, 2026
Snowflake, BigQuery, Redshift, or Databricks? Every Amazon seller moving beyond Seller Central's limited reports faces this question. The wrong choice means months of migration pain later. This comparison cuts through marketing hype and shows which warehouse actually fits your business.
After helping 200+ Amazon sellers implement data warehouses, we've learned that the "best" choice depends on factors that vendor comparison charts ignore: your existing tech stack, team skills, and actual analytics needs.
This guide compares the four major options across the dimensions that matter for e-commerce data. No theoretical benchmarks. Real-world performance with Amazon seller data. Honest cost breakdowns including hidden fees.
Easiest to use, best dbt ecosystem, works with any BI tool. The default choice unless you have specific requirements for alternatives.
If you run Google Ads, use GA4, and build Looker dashboards, BigQuery's native integrations win. Simpler serverless model too.
Already all-in on AWS with Glue, Lambda, and S3? Redshift consolidates billing and IAM. Reserved pricing can beat alternatives.
Building demand forecasting or pricing models? Databricks excels at ML workloads. Overkill for standard reporting.
| Feature | Snowflake | BigQuery | Redshift | Databricks |
|---|---|---|---|---|
| Serverless | Hybrid | Yes | Optional | Yes |
| Pricing Model | Compute + Storage | Per Query + Storage | Node Hours | DBU + Cloud |
| dbt Support | Excellent | Excellent | Good | Good |
| ML Capabilities | Snowpark ML | BigQuery ML | SageMaker | Native (Best) |
| Multi-Cloud | AWS/Azure/GCP | GCP Only | AWS Only | AWS/Azure/GCP |
| Learning Curve | Low | Low | Medium | High |
| Concurrency | Excellent | Good | Limited | Good |
| Data Sharing | Native | Analytics Hub | Data Sharing | Delta Sharing |
Snowflake has become the standard for cloud data warehousing. For Amazon sellers without strong preferences toward other platforms, it's the safest bet.
Snowflake charges separately for compute (credits) and storage. Here's what Amazon sellers typically pay. See Snowflake pricing for current rates.
$80-150
Per month, XS warehouse
$300-600
Per month, S/M warehouse
$1,500-4,000
Per month, multiple warehouses
Set auto-suspend to 60 seconds. Amazon analytics workloads are bursty. A warehouse running 24/7 costs 10x more than one that auto-suspends between queries. This single setting can save thousands annually.
If your marketing runs through Google (Ads, Analytics, Looker), BigQuery's native integrations provide significant advantages.
BigQuery charges per TB of data scanned. Storage is separate. The first 1TB of queries per month is free. See BigQuery pricing for details.
$30-80
Per month, on-demand
$150-400
Per month, on-demand
$800-2,500
Per month, flat-rate better
Always partition Amazon data by date. A query scanning 2 years of orders costs 30x more than the same query filtered to last 30 days. Partitioning is free and automatic. Just add a date column as the partition key.
If your infrastructure runs on AWS (EC2, S3, Lambda, Glue), Redshift offers ecosystem consolidation that matters more than feature comparisons suggest.
Redshift offers both provisioned (fixed nodes) and serverless (pay-per-use) options.
$100-200
Per month, variable usage
$360
Per month, on-demand
$1,500-5,000
Per month, managed storage
Nova delivers clean Amazon data to any warehouse. Focus on analysis, not pipeline maintenance. Get 900+ metrics flowing in days.
For a deep dive, see our complete Redshift guide for Amazon sellers.
Databricks is not a traditional data warehouse. It's a unified analytics platform optimized for machine learning. Most Amazon sellers don't need it.
Databricks charges in Databricks Units (DBUs) plus underlying cloud costs.
$500-800
Per month, 100 hours
$400-600
Per month, 50 hours ML
$1,200-2,200
Per month, production use
For implementation details, see our complete Databricks guide for Amazon analytics.
Use this framework to decide. Answer honestly about your current situation, not aspirational goals.
| If You... | Choose | Because |
|---|---|---|
| Have no strong platform preference | Snowflake | Best ecosystem, easiest to learn |
| Run Google Ads + GA4 + Looker | BigQuery | Native integrations, unified marketing data |
| Are all-in on AWS infrastructure | Redshift | Ecosystem consolidation, RI savings |
| Need demand forecasting ML | Databricks | Best ML platform, feature store |
| Want dashboards without infrastructure | Nova | No warehouse needed |
| Manage multiple brands (aggregator) | Snowflake | Data sharing, multi-tenant architecture |
The warehouse choice matters less than how you get Amazon data into it. The Selling Partner API is complex, rate-limited, and constantly changing.
| Approach | Timeline | Coverage | Maintenance |
|---|---|---|---|
| DIY SP-API Pipeline | 6-12 months | As built | High ongoing |
| ETL Tools (Fivetran, Airbyte) | 2-4 weeks | Limited | Medium |
| Nova Data API | Days | 900+ metrics | None |
Most warehouse projects fail at data ingestion, not analysis. The SP-API has 20+ endpoints with different authentication flows, rate limits, and data formats. Settlement reports alone have 200+ transaction types to parse. Budget 80% of your effort for data ingestion, 20% for analysis.
Whatever warehouse you choose, follow these architecture patterns for Amazon seller data. For detailed implementation guides, see our warehouse architecture guide.
Raw data, minimal transformation. 1:1 with source systems. Never delete staging data.
Cleaned, deduplicated, joined. Business entities emerge. Light transformations.
Business-ready tables. Dimensional models. Dashboard-ready aggregations.
dbt works with all four warehouses and makes your transformation logic portable. Key benefits:
Analysis paralysis costs more than picking the "wrong" warehouse. All four platforms handle Amazon data competently. The differences matter less than getting started.
Our recommendations:
Whatever you choose, the hardest part is getting Amazon data in reliably. The warehouse is just a container. The real value comes from clean, accurate, timely data. That's the problem Nova's Data API solves.
See your Amazon performance in Nova.
Try for free