Analyze your scientific data without stitching together five tools.
Small labs and research teams working with scientific data (genomics, physical signals, climate series) normally assemble their analysis by hand with Python and several libraries. We give them a single engine that already has that analysis built in and reads their file formats directly.
Let's talk about your research projectAn open mission for applied science
linalDB, our analytical engine, is free for research and education, because applied science shouldn't depend on an infrastructure budget. If your project involves a university-industry partnership, Gorigami can act as the national industry partner.
See linalDB's documentation →Communities already running this:
- Universities and small labs
- Genomics
- Climatology
- Astronomy
- Systems and control engineering
Against the usual stack for this, the gap looks like:
The mechanics, in short:
How we implement it
USE DATASET FROM "file.h5" or .nc for direct ingestion, PCA TO COMPONENTS k / SVD / EIGEN for the analysis, all persisted with automatic lineage and no extra scripts.
Before we talk, the honest limit:
What we don't solve
We don't replace specialized scientific visualization tools (advanced matplotlib, domain-specific plotting) or distributed compute for datasets that don't fit in a single machine's memory: Dask or Spark are still needed there. We're strong for analysis that fits on one node, not for massive scientific compute clusters.
Frequently asked questions
Does linalDB replace Python/numpy?
Not necessarily. You can keep using Python for what you already have, and linalDB for what you currently do in a separate notebook with no traceability.
Does it work with my NetCDF or HDF5 files as-is?
Yes, ingestion is direct, with no prior conversion to another format.
Does it work for very large datasets?
As long as they fit in a single node's memory, yes. For massive distributed compute clusters, it's not the right tool: Dask or Spark are still needed there.