Extract, validate and transform. One environment for your whole pipeline
Extract, transform and load (ETL) on the same canvas — with real-time change capture straight from the database log. You write the SQL; the incremental logic is on Januss.
Real-time change capture comes from the entry plan on — it is not an enterprise add-on.
You are billed per row written to the destination — not for what the pipeline had to read to get there.
Every plan has a maximum your bill will never exceed. Not a marketing promise — it is the contract.
Simulate your volume and see the plan, the overage and the ceiling before you sign.
Calculate my planConnects where your data already lives — and delivers where it will be used.
The sync mode is chosen per table or view. The same source can have tables on full load, incremental and change capture at the same time.
Sources
Databases, spreadsheets and files — plus any REST API you point us at
Destinations
Databases and warehouses — they take the load, transform and serve views
Object storage — Parquet, CSV or JSONL, to read straight from the path
Lakehouse — parquet in your bucket, with transformation and upsert
Each destination gets DDL written in its own native dialect.
Integrating data shouldn't consume your team.
Whoever looks after the data ends up stuck in the same loop: a script someone wrote, a schedule nobody reviews and an expensive tool in the middle. The outcome is always the same — late data, numbers that don't add up, and expensive people firefighting.
Scripts that break silently
Every integration turns into a schedule plus a script nobody wants to maintain. When it breaks, you find out from the wrong report — not from an alert.
Expensive people maintaining plumbing
Hours that should become product become pipeline maintenance. It's the cost that shows up on no invoice — and it's the biggest one.
Database passwords in plain text
A production credential stored in the clear inside the integration tool is an incident waiting for a date.
Data that can't leave
Compliance keeps your database from touching the cloud — and most SaaS tools require exactly that to work.
Januss brings the pieces into one visual flow: from source to destination, with SQL transformation running inside your own database, credentials the platform never sees, and a Runner for when the data can't leave your network.
The whole pipeline, under control.
From source to destination — model, schedule and watch every step without leaving the canvas.
Pipelines you draw
Build the extract, transform and load flow by dragging steps. The graph is validated before it runs and executes level by level, in parallel where it can.
CDC — the database tells you what changed
Without it, finding what changed means re-reading the entire table on every run. Januss reads the database's own transaction log — with no extra piece of infrastructure to maintain. If the log rotates before it's read, the reload is automatic.
SQL transformation, in your destination
Transform steps run as SQL inside the destination database itself. No cluster to spin up, no moving the data twice — and only the affected scope is reprocessed.
Every table at its own pace
Full load, incremental, mirroring and versioned history — chosen per table or view. The same source can run all four at once.
Bad data doesn't get through
Declare what has to be true — not null, unique, accepted values, referential integrity, or a query of your own. If it doesn't hold, the pipeline stops and bad data doesn't move on. No external tool, no parallel project.
It tells you before you find out
Failed? You get an email with the error and a direct link to the run — and another when it recovers. After three consecutive failures the pipeline shuts itself off, instead of burning through your allowance failing silently.
Three steps from source to data that's ready.
Point at source and destination
A database, a spreadsheet, an API or a directory. Januss reads the catalog — tables and columns — and tests the connection before letting you save.
Say what the data needs to become
Map columns, chain cleanup rules and write the transformation in SQL. The sync mode is chosen table by table.
Schedule it and forget it
Set the schedule and preview the next runs before saving. Every run records status, duration and rows per step — and a failure sends an email.
Connect any API — Januss writes the connector.
Point it at the OpenAPI spec or at the documentation itself. Januss generates the connector and the table catalog, works out authentication and pagination, and validates with a real call before saving — with no glue code from you.
- Reads OpenAPI and Swagger — or the documentation, when there's no spec
- Six authentication methods, including OAuth2 and token login
- Pagination and nested responses resolved on their own
- It only saves after making the real call and successfully reading what came back — a broken connector never enters the catalog
The AI writes the connector once. After that it is deterministic: extraction runs the same way every time, with no AI in the data path.
Incremental that never trades away the result.
On every run, the destination table ends up identical, row by row, to what a full reload would produce. Incremental is a cost optimization — never a trade in guarantees. And the transformation runs as SQL inside the destination database: technically that is ELT, not classic ETL — the data never leaves for a separate engine and comes back.
- Reprocesses only the scope that changed — and deletes what disappeared, which is where upsert alone leaves debris behind
- When the scope can't be isolated, it reconciles everything rather than get it wrong — and tells you why
- SQL executed in the destination database: no cluster to provision, pay for and maintain
- Chainable cleanup rules per column — currency, date, decimal, text, regular expression, value maps — with an error policy set column by column
MERGE INTO gold.customer_360 t USING silver.customers s ON t.id = s.id WHEN MATCHED THEN UPDATE SET … -- incremental: only the affected scope is reconciled
What fails a test doesn't move on.
Declare what has to be true and let Januss stop the pipeline when it doesn't add up — whether a dashboard, an API or a model will consume that data. No external tool, no parallel project to maintain.
Each test can stop the pipeline or just warn — you decide, test by test.
Your credentials — not even we can see them.
Your database passwords are encrypted in your browser, before they leave your machine. The platform stores only the encrypted package and never opens it — only the executor does, at the moment of execution.
- End-to-end encryption — the password never travels or rests in the clear
- SSH tunnel for databases not exposed to the internet
- Execution stays in your workspace's region — Brazil or the United States
- And when the data can't leave your network, the Runner handles it
The Runner is installed inside your network and talks to Januss over a single outbound connection on port 443. Extract, transform and load happen in there — the data never passes through the cloud. No inbound firewall rules.
Know your bill before you sign.
You pay for the rows written to the destination, across every step that writes. Each plan includes a volume, charges per additional million — and has a ceiling your bill never passes.
Rows written per month
10 million
Growth
$199/mo
| Plan | Rows included | Minimum interval | History | Monthly | Additional million | Bill ceiling |
|---|---|---|---|---|---|---|
| Starter | 1M | 1 hour | 7 days | $59 | $18 | $131 |
| Growth | 10M | 15 minutes | 30 days | $199 | $12 | $379 |
| Pro | 50M | 5 minutes | 6 months | $549 | $6 | $699 |
| Business | 100M | 5 minutes | unlimited | $899 | $3 | $1,499 |
| Enterprise | custom | custom | unlimited | on request | — | negotiated |
Every plan includes real-time change capture, all sources and all destinations. Each plan's ceiling is always lower than the next plan's monthly price — going over on volume never costs more than moving up a tier.
How usage is counted
It counts every step that writes to the destination — not just the extraction. Wrote 1M on the load and another 1.2M across two transformations? That is 2.2M of usage in the cycle, even though the source is the same.
Each plan includes a fixed allowance of rows. Anything above it is charged per additional million — and you track usage live on your dashboard.
Illustrative — real usage shows live on your dashboard.
Let's see this running on your data.
We talk through your scenario, show the platform running and — if it makes sense — put the first pipeline live together with your team. You talk directly to the people who build the product.