Operational tables for search, SQL, notebooks, AI and more
Continuously stream data from databases, SaaS applications, files, and event sources into your warehouse or lakehouse, delivering low-latency, reliable pipelines without the operational overhead.
Your operational tables
Everything, in one place
Your analytics tables
Works with what you already run.
No proprietary formats, no lock-in, no "supported integrations" page. Streambased connects the databases that run your business to the engines that answer your questions, through open standards your stack already uses. Compatibility is the product.
Connect once. Query forever.
No new storage system to learn. No architecture diagrams to study. Three steps, and every event you own is at your fingertips.
Connect
Point Streambased at the systems your data already lives in. No migration, no copies, no pipelines. Everything stays exactly where it is.
Discover
Every topic, schema, and table is detected automatically and appears in one searchable catalog. Nothing to configure, nothing to sync.
Use
Search, query, notebook, replay, build. Streaming and historical data behave like one dataset, because now they are.
One dataset. Consumed as a stream or a table.
Applications shouldn't have to choose between real-time and analytical access. The same underlying dataset can be consumed as an event stream for reactive systems or queried as a table for analytics, reporting, and ad-hoc exploration. Different interfaces, one source of truth.
- Don't copy your data into yet another system.
- Don't transform it unless you need to.
- Don't build pipelines just to make it usable.
See what disappears
The usual path from an operational database to your analytics stack is a chain of systems. Streambased collapses it to a single hop.
Every hop is another system to run, another copy to keep in sync, another place it can break.
Give your streaming data a home
From operational databases to your analytics stack, Streambased makes everything queryable from one place with no pipelines in between.