Query Amazon S3 by any set of keys at high-throughput and milliseconds-latency using a REST API, without the overhead of managing any additional data stores.
More data in memory compared to NoSQL databases
Reads per second, per server, at 1ms latency on average
Faster time to production for real-time analytics and machine learning
Lookup Tables add indexing at high cardinality and performance to your data lake. They enable users to index data by a set of keys and then retrieve the results in milliseconds. Unlike NoSQL alternatives, Upsolver’s ETL platform stores indexed data on S3 rather than local servers, which turns IT-intensive cluster management into a non-issue. Lookup Tables leverage break-through compression technology and smart rollups that enable 10X-15X more data in-memory compared to alternatives.
Reduce 70-90% of infrastructure costs by storing more data in RAM
Decoupled compute and storage for easy healing, scaling, and disaster recovery
Easy to create without ETL coding or IT management using aself-service UI and UpSQ
Lookup Tables are stored on S3 as a time-series. Using smart rollups, Upsolver makes it possible to query any time range by any set of keys.
Capture real-time behaviour for users and devices, using window aggregations, nested aggregations and time-series aggregations.
Embed granular user or device data in your applications using a simple REST API and avoid the overhead of additional data stores.
Serve data in sub-second latency to power real-time dashboards and data-driven apps
Improve machine learning models and performance by adding user or device-level behavioral features
Perform joins between multiple streaming sources or big tables with a high number of keys.
Enforce GDPR and other regulatory compliance by keeping track of all user records in your lake
