Getting Started with Keboola

Keboola is a platform for moving data around and doing something useful to it on the way: pull it out of the systems that hold it, reshape it, put the result where people need it, and keep doing that on a schedule without anyone watching. This guide walks you through that whole loop once, on sample data, in under an hour.

By the end you will have a pipeline that is genuinely running. Not a demo — the same mechanism a production project uses, just smaller.

What you will build

Phase	What happens	Keboola calls it
Load	four CSV files become four tables	a data source connector
Transform	SQL joins them into one wide table	a transformation
Deliver	that table appears in a Google Sheet	a data destination connector
Automate	all of it runs daily, in order, and emails you if it breaks	a flow

The data is a small sales pipeline: opportunities, the accounts they belong to, the users who own them, and each user’s seniority. The join produces the table someone would actually want to look at — every opportunity with its account, its owner, and how likely it is to close.

Before you start

• A project. Step 1 gets you one; the Free Plan is enough for everything here.
• Basic SQL. One SELECT with a couple of JOINs. If you have never written SQL, the queries are given in full and you can paste them — in both Snowflake and BigQuery form, since which one you need depends on your project.
• A Google account, for the delivery step. If you would rather not connect one, stop after step 3 — you will have seen the core of the platform, though you will not have a pipeline to automate in step 5.

Nothing needs installing. Everything below happens in the browser.

New to the platform entirely?

If you would rather understand the pieces before touching them, read the platform overview first — what the components are, how Storage is organized, what a stack is. This guide explains each piece as it comes up, so you can also just start.

The steps

1. Get a Project — create or join one, learn what a project and a stack are, find your way around.
2. Load Your Data — upload the four sample files into Storage and understand buckets, tables and stages.
3. Transform Your Data — write the SQL that joins them, and see how input and output mapping keep your source data safe.
4. Send Your Data Somewhere — push the result to a Google Sheet with a data destination connector.
5. Automate It with a Flow — run the whole thing in order, on a schedule, with notifications.
6. Where to Go Next — what to learn next based on what you actually want to do, including how to drive Keboola from an AI assistant, an IDE, or your terminal.

Read them in order. Each step ends with a link to the next, and every page states what it assumes so you can also land on one directly and catch up.

Going further

Optional side trips, once the main path makes sense. None of them are needed to finish the arc:

• Load from Google Sheets and Load from a Database — do the loading step the way a real project does it, with a connector instead of a manual upload.
• Use a Workspace — develop and test SQL against a copy of your data before committing it to a transformation.
• Ad-Hoc Data Analysis — explore arbitrary data in a Python or R notebook rather than building a pipeline.
• Development Branches — change a running project safely, review the diff, then merge.

If you are planning a rollout, not learning the tool

This guide is for one person building one pipeline. For introducing Keboola to a team — project architecture, a data model, naming conventions, governance — start with Platform Onboarding instead.

Next: Get a project →