Think "data lake of one"
Each Micro-Database acts as a governed “data lake of one”: complete operational context for a single customer, order, claim, or device, isolated from every other entity and synchronized with source systems for governed read/write access.
Isolated, for security and scale
Each Micro-Database is isolated from every other Micro-Database and governed independently. This entity-level boundary prevents unauthorized cross-entity access, limits data exposure, and supports high-concurrency workloads without cross-entity contention.
Separate, for security and scale
Separation means security. All Micro-Databases are isolated from each other and individually secured, each with its own unique encryption key. This makes data leakage and mass data breaches virtually impossible.
Separation also means scale, since there is never any contention between Micro-Databases, even at a scale of thousands of concurrent queries.
Built for real-time, governed context
Keep each Micro-Database aligned with source systems, so entity context stays current without being rebuilt for every request.
Give authorized consumers low-latency access to entity-level context through SQL, APIs, CDC, messaging, streaming, MCP, and other delivery methods.
Encapsulate each entity’s context in its own Micro-Database, enforcing boundaries for access control, exposure, and concurrency.
Support governed retrieval and authorized updates, with K2view data products orchestrating write-back to source systems through approved actions and policies.
Run Micro-Databases cached, persisted, virtualized, or in memory to balance latency, freshness, cost, and workload requirements.
Let generative data products do the job
Let generative data products do the job
Generative data products manage billions of Micro-Databases at once. Define the metadata your data product needs to meet its goals, including:
- Micro-Database schema per business entity
- Data integration and delivery methods
- Sync rules, data transformation logic
- Governance policies
Build and evolve Data Products in action
Continuous Data Product Evolution & Schema Changes
Adapt data products to schema changes without breaking the consumers that depend on them.
Adapt data products to schema changes without breaking the consumers that depend on them.
How K2view improved Vodafone customer experience
"K2view continually ingests data from our enterprise systems, unifies it by business entity, and saves each one into its own Micro-Database. Millions of Micro-Databases are managed concurrently"
"K2view continually ingests data from our enterprise systems, unifies it by business entity, and saves each one into its own Micro-Database. Millions of Micro-Databases are managed concurrently"








