Cube’s semantic layer enables cost-effective work with billion-row datasets and subsecond responses while orchestrating caching upstream of all of your data apps—once.
Caching
Data Modeling
Access Control
APIsCube’s two-level caching system uses an in-memory cache, queue management, and configurable pre-aggregations. Cube’s in-memory cache and queue management capabilities are based on our proprietary Cube Store; together, they serve as a buffer for your database when there’s an influx of concurrent requests hitting your database, allowing you to scale stateless API instances horizontally and making APIs idempotent.
Cube's caching layer comes with configurable pre-aggregations, a layer of aggregated data built and refreshed by attributes and intervals you set. Access the condensed, cached version of your data to maximize speed and minimize latency.
Why spend unnecessary capital (and time) to query operationalized data directly from data sources? Redundant queries result in latency and major cost-inefficiency. Streamline your pipeline by querying Cube’s cache—and watch your bills drop.
Since Cube is a middleware that sits between your data source and applications, it’s upstream of every data app your organization uses. Therefore, its position in the data pipeline means that you don’t need to manually orchestrate caching for every application, saving you effort and ensuring that your applications are uniformly performant and coordinated.
Fully managed hosting of your Cube apps
With Cube, we’ve reduced the time required to generate real-time and historical reports from 10’s of seconds to less than 2, while reducing our spending on hosting by almost 80%.
Brandon Ellis
Co-Founder & CTO at Cuboh
With Cube, we’ve been able to speed up time to release a new data model to production by 5x and decrease analytics downtime by 90%.
Alessandro Lollo
Senior Data Engineer at Cloud Academy
We found an incredibly fast-to-market and flexible modern analytics solution: Cube. We were able to deliver a complex but highly customized embedded analytics user experience—in two weeks, and without changing our stack.
Dhyan Shah
Product Manager at RamSoft