Upsolver unlocks the value of your AWS data lake by automating labor-intensive data engineering work. Effortlessly build perfect data pipelines with nothing but a visual interface and the SQL you already know.
faster queries against S3
Improvement in data freshness
reduction in ETL development time
Prepare to be astounded by how easy it is to prepare your data streaming.
on any VPC
terabytes to petabytes
all you need is SQL
Effortlessly get all your data on S3, automatically indexed and optimized.
Natively connect to message brokers and data lakes
Upsolver pulls data directly from your Kafka producer, Kinesis topic or existing object storage – simplifying data lake ingestion and ensuring your data lake stays well-irrigated throughout.
Write, partition and store optimized data on S3
Write all your events exactly-once and store everything on S3: both raw historical data and optimized Parquet/ORC files – with automatic compaction and compression, custom partitioning (by event time and custom fields) and other best-practices baked in to make data ready for consumption.
Autodetect schema-on-read and statistics per field
Immediately get insights into your data – schema, statistics, sparse fields and more. Replace blind ETLs or inaccurate guesstimates with a detailed, comprehensive understanding of your data in real-time.
Orchestrate batch and stream processing without breaking a sweat leveraging Upsolver’s unique data lake indexing technology.
Build with SQL or a visual UI
Replace thousands of lines of codes with a simple SQL statement or literal drag and drop to enrich, transform and aggregate streaming data, including nested JSON.
Join historical and streaming data into a single table/dataset
Easily join multiple streaming sources and historical big data for richer analytics and machine learning, including stateful transformations.
Create a variety of window aggregations including count distinct
Upsolver’s ETL engine is built for the ground-up for streaming data, simplifying complex operations into a simple SQL statement.
Unlock 100x faster queries in Athena leveraging optimized Apache Parquet, and enable sub-second latency for real-time use cases.
Make your Serverless Query Engines better:
Better, faster, and easier queries in Amazon Athena (and Presto): Upsolver continuously optimizes data lake storage and deeply integrates with the Glue Catalog to provide unprecedented performance – provide up to 100x better performance while spending only a fraction of the time on data preparation.
Update and delete data in Athena or S3 in a click: Think data lake upserts are impossible or complicated? Think again – Upsolver lets you instantly update tables in Athena as well as the data stored in S3, without any additional tool and without full table scans.
Historical replay from S3 and automated table edits: With all your historical data stored and indexed on S3, Upsolver makes historical replay instantly available. Leave no data behind!
Enable real-time data serving in a click:
Self-service key-value store that’s set up in seconds: Your real-time architecture just got a thousand times simpler. Use Upsolver Lookup Tables to query the Upsolver serving API by key or by key and time and you’re ready to go.
High concurrency reads with milliseconds latency: Upsolver’s homegrown technology stores compressed indexes in RAM, creating unparalled performance for low-latency, high-throughput scenarios such as real-time decisioning.
Visualize time-series data with Grafana instantly: Create real-time dashboards and alerting by connecting Upsolver’s Lookup Tables to Grafana’s open-source data visualization platform.