Self-service for data consumers
Data analysts and developers can build streaming data pipelines. No Spark/Hadoop required
Frictionless data preparation
Easily prepare terabytes to petabytes of data for analysis, including joins between multiple streams.
Instant access to fresh data
Write your SQL once to continuously get data that is always stays fresh, up-to-date and optimized.
Complex transformations in a familiar syntax
Need to analyze streaming data? Forget about writing hundreds of lines in Scala, clusters and workflow orchestration. If you know SQL, you’re good to go - including native support for nested data, rolling aggregations and window-based joins.
High cardinality joins between streams and data lake storage
With Upsolver, you have a single, SQL-based platform for all your ETL - batch, micro-batch and real-time - and for all your historical and live streaming data, leveraging Upsolver’s unique data lake indexing,
Fresh, up-to-the-minute data without ever updating your query
Write your SQL once and get low-latency data forever. Your query will return fresh data even as your data changes over time, with live data from Kafka available in Athena within seconds to minutes.
Scalable, repeatable workflows
Copy and paste SQL to quickly iterate and create new data pipelines at the speed of your business
Optimized data for consumption with no additional effort
Focus on your analytic workflows while Upsolver does all the data engineering magic under the hood: partitioning, compaction, indexing, and more
Let's get personal: See Upsolver on your data in a live demo.
Schedule a free, no-strings-attached demo to discover how Upsolver can radically simplify data lake ETL in your organization.