What Beetl is

Beetl is a programmable data integration platform. It connects the business systems you already run, transforms what comes out of them with SQL, and makes the validated result queryable, chartable, and reachable from your own tools.

The short version: a lean data platform to enable data-driven teams with AI.

This page walks through five nouns in the order data moves through them, then the places the output goes.

The path data takes

You configure each arrow once, and you can inspect every step afterwards.

Connections bring data in

Data arrives in one of five ways:

• An integration is a connection from an installed Connect package, a short TypeScript program that reads a system's API on a schedule.
• A webhook gives you a token and an endpoint that your system posts JSON to.
• A File Set is a folder of files you upload. A file processor turns them into a dataset, and can read documents such as PDFs into records too.
• A registered dataset reads files already in your object store, in place without copying.
• An external dataset reads a live PostgreSQL table, in place without copying.

Integration code runs in an isolated sandbox inside Beetl and reaches your systems over HTTPS. Beetl runs nothing inside your network. See Connect your data.

Datasets are where data lands

A Dataset is a Delta Lake table in your tenant's object storage, either Beetl's hosted store or a bucket you own. Every write is all-or-nothing, and Beetl maintains the tables automatically.

Each dataset has a tier: Bronze, Silver or Gold. Bronze is what arrived and Gold is what you would hand to someone. The platform does not enforce that order; it is a naming convention your team agrees on. There is no tier lineage DAG, and a Gold table can read from another Gold table.

Beetl checks schemas on the way in and never quietly coerces types. A sync rejects records that do not match its integration's declared schema, and a pipeline run fails before writing if its output does not match the destination. A file processor whose files no longer match its dataset fails and keeps the dataset as it was. See Datasets and tiers.

Pipelines transform

A Pipeline is a notebook. You pick source datasets, write SQL stages against them, preview any stage on real rows, and optionally point the last stage at a destination dataset. Most exploration starts with a pipeline that has no destination at all, and that is a perfectly good thing to build.

Every pipeline has two versions. Working is your editable draft, and Deployed is the version that runs. Save writes the draft, and Deploy promotes it and starts a first run. Keeping them separate means an unfinished edit can never run against production data.

You query the result

The Query page is a multi-tab SQL editor over DataFusion, using the PostgreSQL dialect. It autocompletes columns from the real schema, lets you cancel a long run, and charts what comes back.

Click any cell and Beetl shows the column's role, the SQL expression behind it, and the relations it came from. When Beetl can build a deterministic path back to the contributing rows, it offers to drill into them, and the drill-down is another addressable result.

Every result is saved with its own URL rather than living only on your screen, and it expires after seven days. Read Query results before you start relying on them.

Where the output goes

The first is chat. The assistant sits in a sidebar on every page and also has a full-screen view. It lists datasets, runs queries and builds pipelines through a fixed set of tools. Since August 2026 it can also edit the page you are looking at, on four surfaces.

The second is dashboards. A tenant can add Apache Superset dashboards, which read tenant data read-only and open inside Beetl. You author them in Superset, and they are the only saved, shareable charts in the product.

The third is PDF reports. A report binds a layout to SQL over your datasets and issues PDF documents on demand or on a schedule, which you can view, download and tag.

The fourth is apps. An app is a ready-made application provided by Beetl that reads datasets you choose and publishes its results back as datasets.

The fifth is MCP, which people tend to overlook. Beetl is an MCP server, authenticated with a per-user API key scoped read or write. Claude, Cursor or your own code get the same tools the in-product assistant uses.

Automations run it on a clock

An Automation is a recurring investigation you describe in plain language. It runs on a cron schedule with read-only queries and returns a Markdown report, along with every query it ran and why. It can only observe: an Automation cannot propose or change anything.

Where to go next