Head-to-head · Data Marketplace Software
Data Marketplace vs BigQuery sharing
Data Marketplace leads on 0 checks, BigQuery sharing on 0, and 5 are even. Who comes out ahead on the 5 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.
Our verdict
- Highest scoreData Marketplace · 9.4/10
- Free planonly Data Marketplace
- Most featuresBigQuery sharing · 4 of 4
Data Marketplace scores higher on our rubric for data marketplace software: 9.4 against 8.6 out of 10; our editors rank them #1 and #7.
On commercial model, BigQuery sharing gives you Custom where Data Marketplace offers Subscription.
Data Marketplace is the better fit for affordable general-purpose data marketplaces. BigQuery sharing is the better fit for bigQuery-native data sharing.
- Data Marketplace fits best
Affordable general-purpose data marketplaces
- BigQuery sharing fits best
BigQuery-native data sharing
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Side by side
| Feature | Data Marketplace 9.4/10 Visit ↗ | BigQuery sharing 8.6/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 9.4 | 8.6 |
| Ranking | #1 in Data Marketplace Software | #7 in Data Marketplace Software |
| Best for | Affordable general-purpose data marketplaces | BigQuery-native data sharing |
| Pricing model | Free plan + paid | Paid |
| Starting price | $19/mo | Pricing on request |
| Free plan | ✓ (best) | Not checked yet |
| Free trial | — | — |
| Deployment | Cloud | Cloud |
| Platforms | Web | Web |
| Support | Docs | Live chat, Phone, Tickets, Community, Docs |
| Built for | Solo, Small business, Mid-market, Enterprise | Small business, Mid-market, Enterprise |
| Features Data Marketplace 3/4 · BigQuery sharing 4/4 | ||
| Data transactions | ✓ | ✓ |
| Seller publishing | ✓ | ✓ |
| Access controls | ✓ | ✓ |
| Private marketplace | Not checked yet | ✓ (best) |
| Specs | ||
| Delivery methods | Not checked yet | Query |
| Dataset listing limit | Not checked yet | Not checked yet |
| Commercial model | Subscription | Custom |
| Our review | ||
| Pros |
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| Cons |
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| Our verdict | Data Marketplace is a software marketplace for analysts, teams, data providers, and AI agents that need to discover, evaluate, publish, license, purchase, and deliver datasets. Its buyer-facing capabilities include dataset search,… Read the review → |
BigQuery sharing, formerly Analytics Hub, is a Google Cloud data exchange platform for publishing, discovering, subscribing to, and querying shared datasets and Pub/Sub topics. Providers can organize resources into public or private… Read the review → |
Strengths and trade-offs
Data Marketplace — where it wins
- Free plan plus a $19/month Starter tier
- PII reports, quality scoring, previews, and data dictionaries
- Supports downloads, REST API, MCP, and query-based access
Where it doesn't
- Free plan is limited to 10 downloads and 1,000 API calls monthly
- Professional costs $99/month before Enterprise pricing
- Enterprise pricing is by agreement rather than a published amount
BigQuery sharing — where it wins
- Shares datasets and Pub/Sub topics through public or private exchanges
- Enables read-only linked datasets without replicating underlying data
- Combines IAM controls, subscriptions, usage tracking, and Marketplace sales
Where it doesn't
- Pricing follows a contact-sales model rather than a published fixed plan
- Subscribers and publishers may incur underlying service-related charges
- Best suited to organizations already operating in the Google Cloud ecosystem
- Data Marketplace9.4/10 · Free plan
A broad data marketplace with discovery, licensing, delivery, and query access.
Try Data MarketplaceFull verdict → - BigQuery sharing8.6/10 · Pricing on request
A BigQuery-native exchange for governed data sharing, discovery, and direct querying.
Visit Google CloudFull verdict →
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- Data Marketplace vs Narrative Data Marketplace
- Advaneo DataMarketplace vs BigQuery sharing
- AWS Data Exchange vs BigQuery sharing
All data marketplace software comparisons → · Full ranking →
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Last updated · How we research and update







