Data Engineering
Snowflake or BigQuery? For Amazon sellers moving beyond Seller Central's reports, this decision shapes your analytics stack. Real cost comparisons, ecosystem integration, and decision framework.
Dec 5, 2025 · 8 min read · 1,572 words
Updated October 14, 2026
Snowflake or BigQuery? For Amazon sellers moving beyond Seller Central's limited reports, this decision shapes your analytics stack for years. Both handle Amazon data well. Both connect to your BI tools. But they excel in different scenarios. This guide breaks down the real differences that matter for e-commerce analytics.
Your Amazon data needs a home. A real home, not another spreadsheet or Seller Central's 60-day data retention prison. The two leading cloud data warehouses, Snowflake and Google BigQuery, both promise to solve this problem. But the "best" choice depends entirely on your existing stack, team skills, and how you plan to use the data.
A tool that wins on paper can lose in practice. This comparison focuses on the practical differences that actually impact Amazon seller analytics workflows.
| Factor | Snowflake | BigQuery | Winner For |
|---|---|---|---|
| Pricing Model | Compute + storage separate | Per-query + storage | Depends on usage pattern |
| Best For | Heavy concurrent workloads | Ad-hoc analysis, ML | Different use cases |
| dbt Support | Excellent, most popular | Excellent | Tie |
| Google Ecosystem | Third-party integrations | Native Looker, GA4, Ads | BigQuery |
| Multi-Cloud | AWS, Azure, GCP | GCP only | Snowflake |
| Learning Curve | Standard SQL, more concepts | Standard SQL, serverless | BigQuery (simpler) |
Don't ask "which is better." Ask "what does my team already use?" If your company runs on Google Cloud with Looker dashboards, BigQuery wins. If your data engineers live in Snowflake and dbt, that's your answer. Switching costs exceed any marginal performance difference.
Both platforms advertise competitive pricing. The reality? Your bill depends entirely on how you use Amazon data. Compare both platforms with your own workload because architecture and usage determine the bill.
Snowflake charges separately for compute (credits) and storage. You pay for warehouse runtime in credits, regardless of query complexity. According to Snowflake's pricing documentation, credit costs vary by cloud provider and region.
$2/hour
1 credit/hour, good for small queries
$16/hour
8 credits/hour, dashboard workloads
$23/TB
Per month, compressed data
Set aggressive auto-suspend (60 seconds) on warehouses. Amazon data workloads are typically bursty: heavy during ETL, light during reporting. A warehouse running 24/7 at XS costs $1,440/month. The same warehouse with proper auto-suspend might cost $50-100.
BigQuery charges per query based on data scanned. Storage is separate. The first 1TB of queries per month is free. See BigQuery's pricing page for current rates.
$6.25/TB
Pay per TB scanned
$2,000/mo
100 slots, unlimited queries
$20/TB
Active storage per month
Partition your Amazon data by date. A query scanning 2 years of orders at 50GB costs $0.31. The same query partitioned and filtered to last 30 days at 2GB costs $0.01. That's a 31x difference on the same data.
Let's model actual costs for an Amazon seller doing $50K/month with 500 SKUs and a 3-person analytics team running daily reports.
| Cost Component | Snowflake | BigQuery |
|---|---|---|
| Storage (10GB Amazon data) | $0.23 | $0.20 |
| Daily ETL (1 hour XS) | $60/month | $15/month (queries) |
| Dashboard queries (ad-hoc) | $30/month | $25/month |
| Total Monthly | ~$90 | ~$40 |
For small to mid-size Amazon businesses with light analytics workloads, BigQuery's on-demand pricing typically wins. But this flips at scale. Let's see the $5M seller scenario.
A $5M annual seller with 5,000 SKUs, 15-person analytics team, and heavy concurrent dashboard usage.
| Cost Component | Snowflake | BigQuery |
|---|---|---|
| Storage (500GB) | $11.50 | $10 |
| Daily ETL (4 hours Medium) | $1,920/month | $400/month |
| Heavy dashboard queries | $500/month | $2,500/month |
| Total Monthly | ~$2,430 | ~$2,910 |
At this scale, the cost difference is negligible. The decision should be based on team expertise and ecosystem fit, not pricing.
If you're running Google Ads, using GA4, and building Looker dashboards, BigQuery is the obvious choice. The native integrations are seamless. According to Google's Analytics Hub documentation, data sharing between Google properties is automatic.
For Amazon sellers running significant Google Ads alongside their Amazon advertising, BigQuery lets you build unified CAC and ROAS dashboards without complex data movement. Learn more about advertising cost tracking that works across platforms.
Snowflake dominates in multi-cloud enterprise environments where data needs to flow between AWS, Azure, and GCP. The Snowflake Marketplace provides access to third-party datasets for enrichment.
For aggregators managing portfolios across multiple brands, Snowflake's data sharing capabilities enable secure cross-brand analytics without data duplication. See how aggregators use Nova to centralize portfolio analytics.
Our data engineering team has implemented Amazon analytics on both Snowflake and BigQuery. Get a personalized recommendation based on your existing stack.
Both platforms handle Amazon data fine. But some Amazon-specific requirements favor one over the other.
Amazon's SP-API has rate limits and reporting delays. Most data is available within 15-30 minutes. Both warehouses can handle this refresh cadence, but the ingestion architecture differs.
| Data Type | Snowflake Approach | BigQuery Approach |
|---|---|---|
| Orders for orders and units | Snowpipe continuous loading | Streaming inserts or batch |
| Settlement reports | Daily batch via COPY | Daily batch load jobs |
| Ad performance | Incremental merge | Partition overwrite |
Amazon settlement reports are notoriously complex with 200+ transaction types. Both warehouses handle this fine, but the transformation approach matters more than the warehouse choice. Check out our guide on data warehouse architecture patterns for Amazon data.
Don't try to parse settlement reports with warehouse SQL alone. Use a transformation layer like dbt to categorize the 200+ fee types into actionable categories (referral fees, FBA fees, advertising, adjustments). The warehouse doesn't care about this complexity. Your data model does.
Both Snowflake and BigQuery have excellent dbt support. In fact, dbt makes the warehouse choice less important because your transformation logic becomes portable. Learn more from the dbt documentation on supported platforms.
Snowflake has a larger dbt community overall, but both platforms support the packages relevant to Amazon sellers:
| Package | Snowflake | BigQuery | Amazon Relevance |
|---|---|---|---|
| dbt-utils | Full support | Full support | Essential for all projects |
| dbt-date | Full support | Full support | Date spine for gaps |
| dbt-expectations | Full support | Full support | Data quality tests |
| fivetran/amazon_ads | Native | Native | Ad data models |
While dbt code is portable, optimization strategies differ:
Already on one platform and considering switching? Here's the migration reality.
Warehouse migrations are expensive. Plan for 3-6 months of parallel running, extensive testing, and hidden costs. The 20% cost savings you're chasing will be consumed by migration effort. Only migrate if there's a compelling ecosystem reason, not marginal cost savings.
| Component | Snowflake → BigQuery | BigQuery → Snowflake |
|---|---|---|
| Schema migration | 1-2 weeks | 1-2 weeks |
| dbt model conversion | 2-4 weeks | 2-4 weeks |
| BI dashboard rebuild | 4-8 weeks | 2-4 weeks (if Tableau) |
| ETL pipeline updates | 2-4 weeks | 2-4 weeks |
| Testing & validation | 4-8 weeks | 4-8 weeks |
| Total | 3-6 months | 3-5 months |
Use this framework to decide. Answer honestly, not aspirationally.
After all the benchmarks and cost comparisons, the best data warehouse is the one your team actually uses. A perfectly architected Snowflake implementation gathering dust is worse than a scrappy BigQuery setup driving daily decisions.
For most Amazon sellers starting fresh, we recommend Snowflake for its ecosystem breadth and dbt community. If you're already in Google's ecosystem, BigQuery is the obvious choice. Both will serve you well for years.
The harder problem is getting Amazon data into your warehouse reliably. That's where most projects fail. Whether you choose Snowflake or BigQuery, consider how you'll solve the data ingestion challenge before committing to a warehouse.
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