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access to a provider’s data. Although traditional data warehouses and data lakes (repositories that store massive amounts raw data until needed for analysis) were designed to make data usable, their underlying architectures are not capable of modern data sharing — providing data access to data consumers without having to move the provider’s data.
Burdens of old methods of
data sharing
Traditional data sharing is slow, and it reduces an enterprise’s ability to execute quickly. In addition, a lack of security and governance, among other things, means traditional data ware- houses and data lake architectures cannot support unlimited concurrent access by data consumers or real-time data changes by data providers without cumbersome unloading and transfer- ring of data, as shown in Figure 4-1. This puts data consumers at risk of operating on stale (static) data.
FIGURE 4-1: Traditional data sharing requires cumbersome, multi-step processes by data providers to deconstruct, encrypt, and send data. For data consumers, they must perform the reverse process on the shared data.
The lack of a comprehensive solution creates a struggle for data providers and consumers to easily share data and ensure data consistency. These barriers also limit the ability to monetize data and create new business opportunities.
24 Data Sharing For Dummies, Snowflake Special Edition
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