· Javier de la Torre
Does your agent need a data warehouse?
Start with the compute you already have. An agent running DuckDB and a set of shared Apache Iceberg tables are often enough to begin, and a managed warehouse remains the right choice for the workloads that need one.
A cloud data warehouse is a useful way to run analytics. But it does not have to be the starting point for every project.
If your agent can run DuckDB on a laptop, a server or an execution environment, it already has somewhere to perform analysis. The next question is where the data lives, and what happens to the results when that session ends.
Separate the data from the session
Local files are a convenient starting point. They become harder to manage when several sessions, agents or colleagues need to work with the same data.
A shared catalog and persistent storage give those workflows a common place to read from and write back to. An agent can load a dataset, analyse it and save a useful result as a table. Another authorised session can pick it up later.
That is the role LakehouseBox is built to serve: Apache Iceberg catalogs and storage, with access management and table maintenance.
Use the compute you already have
With DuckDB running in your agent's environment, queries execute there. LakehouseBox provides access to the tables and the underlying storage.
This can be a practical starting point for exploration, periodic analysis and workflows where the available machine can handle the job. It also gives you a straightforward place to begin measuring what your workload actually needs.
Compute still has a cost. Your machine has limits, and moving data across a network takes time. The benefit is the freedom to choose and size that compute independently of where you keep your tables.
A warehouse can still be the right choice
Some workloads need high concurrency, tightly managed infrastructure, demanding latency targets or platform capabilities that a local engine and shared storage do not provide on their own.
For those workloads, a managed warehouse or another compute service may be worth paying for.
Start with the requirements of the job. Measure representative queries, data transfer and operational effort. Choose the setup that serves those requirements.
What changes as agents improve?
Our thesis is that more analytical workflows will be assembled by agents using capable engines and open tables.
The agent can help choose a tool, write a query and save a useful result. The underlying data needs to remain accessible, governed and independent of any one session.
That is the future we are building LakehouseBox for.
Your agents bring the compute. We take care of the storage.
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