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Day 46 of 50
D5: Data Collaboration Week 7
DAY 46

Data Clean Rooms, Marketplace, Listings & Native Apps

Day 45 shared data privately between accounts with shares and reader accounts. Today opens that up to public discovery and privacy-preserving collaboration.

🗣️ Plain-English First
TermWhat it sounds likeWhat it means in Snowflake
Snowflake MarketplaceAn online store for dataA public catalog where providers list data and apps for consumers to query live without copying.
ListingA product pageA share wrapped with metadata such as a title, description, and sample queries, offered to consumers.
Native AppAn app you installA package of data plus runnable code that a consumer installs and runs in their own account.
Data clean roomA locked lab roomA controlled space where two parties analyze combined data without either seeing the other’s raw rows.
ResharingForwarding a fileA consumer passing an incoming listing onward to other accounts, allowed only when the provider turns it on.
🛒

The Marketplace and Listings: Public Data Sharing

Marketplace data is live, not a copy

The Snowflake Marketplace is a public catalog of data products from third-party providers. A consumer who gets a data set queries the provider’s data in place. No file is downloaded and no pipeline is built. The data is read-only and stays current as the provider updates it.

This is the same no-data-movement principle from Day 45. A direct share and a Marketplace listing both grant access without copying. The Marketplace adds public discovery and metadata on top of that base.

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About the Author
Abhay Krishnan

Abhay Krishnan

Senior Data & AI Consultant
Connect on LinkedIn

With over five years of data engineering experience at EY and Infosys, Abhay Krishnan specializes in building scalable data pipelines and cloud warehousing solutions. He is a certified SnowPro Core professional, alongside credentials in AWS and Azure. Abhay created this 50-day track to solve a problem he faced firsthand: the lack of a structured, free resource for Snowflake certification prep. Follow him on LinkedIn for more data engineering insights.

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