Amazon Athena is powerful alone; with Upsolver, it’s a beast. Use a visual and SQL-based interface to easily create Athena tables directly from streaming data stored on S3, and watch your queries run faster than you ever thought possible thanks to Upsolver’s groundbreaking ETL technology and deep integration with the AWS data lake ecosystem.
Upsolver ingests streaming and batch data and stores it on S3 in an optimized file system based on Apache Parquet. Small files are compacted under the hood with zero coding required.
Using nothing but Upsolver’s visual UI, you can choose to partition data by actual event time as well as by custom field within an event stream.
Add or remove columns from any Athena tables and apply these changes retroactively without missing a beat, and with every change instantly reflected in the Glue Data Catalog.
Upsolver lets you skip the latencies generated by batch processing using Spark platforms, and enables you to query streams directly in Athena in real-time.
Create tables using raw historical data stored on S3 in one click for instant historical replay. If the data is there, you can see it in Athena.
Familiar SQL editor allows you to create custom tables on the fly, from two or more disparate event streams, as well as perform joins between streams and large tables – without sacrificing performance.
Build working solutions for stream and batch processing on your data lake in minutes.