a data lake for tensors and tables

At most organizations, scientific data lives in random buckets with no discoverability or governance. Nobody can say what exists, who may use it, or which version an analysis ran on.

Lakehouse catalogs understand tables, not the multidimensional arrays most scientific data comes in — so teams with both end up running two systems, or building their own. Each one pays the same costs:

• No catalog — nobody can say what data exists, where it lives, or whether it is current.

• Credentials everywhere — cloud storage keys spread across laptops, notebooks, and CI jobs.

• No version history — datasets change silently underneath the analyses that depend on them.

• DevOps drag — scientists spend their time operating infrastructure instead of doing science.

Your data can live in AWS, Google Cloud, and Azure at once, and your people and pipelines still sign in once. Arraylake checks who is asking, then vends short-lived credentials scoped to exactly the dataset they asked for — so nobody ever holds a long-lived storage key, and access is revoked in one place.

Arraylake meets you in the tools you already use. Open a repository by name and read it with Xarray, writing inside a transaction that either lands completely or not at all — or attach the Iceberg catalog to DuckDB and query your tables with SQL.

arrays · Python

import arraylake as al
import xarray as xr

client = al.Client()
repo = client.get_repo("my-org/weather-forecast")

# Read the current state of the data — credentials vended by Arraylake,
# no cloud keys in your environment
session = repo.readonly_session("main")
ds = xr.open_zarr(session.store, group="forecast", consolidated=False)

# Write a new forecast cycle as one atomic transaction
with repo.transaction("main", message="2026-08-23 00Z cycle") as store:
    ds_new.to_zarr(store, group="forecast", mode="a", append_dim="time")

tables · DuckDB

-- Point DuckDB at Arraylake's Iceberg REST catalog
INSTALL iceberg; LOAD iceberg;
CREATE SECRET al (
    TYPE ICEBERG,
    TOKEN 'ema_...',
    ENDPOINT 'https://api.earthmover.io/iceberg');
ATTACH 'my-org' AS warehouse (TYPE iceberg, SECRET al);

SELECT station, avg(temp_c) AS mean_temp
FROM warehouse."my-tables".observations
GROUP BY station
ORDER BY mean_temp DESC;

More in the quickstart guide and the Iceberg engines guide.