Query Amazon S3 by any set of keys at high-throughput and millisecond-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 breakthrough 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 a self-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 behavior 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.
Read this case study to learn how Upsolver helped ironSource save thousands of engineering hours and cut costs.
Discover best practices you need to know in order to optimize your analytics infrastructure for performance.
Learn how to avoid common pitfalls, reduce costs and ensure high performance for Amazon Athena.
Instantly improve performance and get fresher, more up-to-date data in dashboards built on AWS Athena – all while reducing querying costs
