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Next-Gen OLAP Technology

Kyvos Smart OLAP™ technology delivers sub-second response times for billion-scale datasets on any BI or data science tool and helps enterprises build a data-driven culture through BI acceleration.



Patented algorithms enable analytical processing on billions of rows to build massive cubes on the cloud. Scale-out incrementally to add new data without any downtime. Enable thousands of users to query the cube for analysis and get results within seconds.


Kyvos profiles data and studies query patterns to build optimized OLAP models. ML-powered Smart Recommendation Engine™ brings intelligence to build multidimensional cubes for desired BI performance.

Highly Performant

Kyvos’ superfast OLAP engine builds massive cubes are fully materialized for speed. Kyvos provides sub-second responses to BI queries across hundreds of dimensions and measures as all combinations are processed in advance.

How is Smart OLAP™ better than Legacy OLAP

Features Legacy OLAP Smart OLAP™
Performance Deteriorates with scale and concurrency. Sub-second responses for queries.
Scale Limitation on amount of data a single cube can hold. Build on billions of rows.
Concurrency Slows down for higher concurrencies. Thousands of concurrent users.
Aggregation Manual tuning required to decide which aggregates to create. ML-powered intelligent aggregates.
Cube Building Cube build takes longer and consumes more resources as data size increases. Quick and easy cube building.
Platform Support Built for on-premise. Has to be retro-fitted in the cloud. Purpose built for the Cloud. Also supports on-premise.

Advanced OLAP Features


Complex hierarchies including ragged, unbalanced, recursive, and alternate hierarchies, and custom rollups.

Distinct Count

Massively scalable accurate distinct count on billions of transactions.

Incremental Builds

Scheduled and automated incremental build for adding and replacing data without coding or manual intervention.

Intelligent Caching

Advanced caching features – auto-population and purging. Multi-level caching based on query patterns and cube analysis.

Elasticity and Auto-Scaling

Schedule cluster scaling to scale up and down without disruption to deal with predictable load changes.


Support for multi-fact, star, snowflake, and other schema designs, as well as additive and semi-additive functions.

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