Where to Go Next
You have a running pipeline — here is what to learn next depending on what you want to do, and how to drive Keboola from an AI assistant, an IDE, or your terminal.
You now have a pipeline that loads data, reshapes it, delivers it, and runs itself on a schedule. That is the whole shape of working in Keboola; everything else is a variation on it. Step 6, the last, of the Getting Started arc.
What you built
Section titled “What you built”| Step | What it does | Where it lives |
|---|---|---|
| HTTP configuration | brings raw data into the project | Components |
| SQL transformation | joins the raw tables into one table | Transformations |
| Google Sheets destination | delivers the result outside Keboola | Components |
| Flow | runs all of it, in order, on a schedule | Flows |
Every job that ran is in Jobs, and every table it touched is in Storage.
Pick your next thing
Section titled “Pick your next thing”“My real data is not a CSV file on a public URL.” Browse the data source connectors — databases, APIs, cloud storage, ad platforms, CRMs. They configure the same way the HTTP connector did, and drop into a flow the same way. Two worked examples are in this guide already: Google Sheets and a database.
“My transformation needs to be more than one query.” Transformations covers SQL, Python, R and dbt, code blocks and phases, shared code, and variables. To develop against a copy of your data interactively, use a workspace.
“I need to send data somewhere specific.” The data destination connectors cover databases, BI tools, and storage — the Google Sheets one you used is the simplest of the family.
“I do not want to break production while I experiment.” Development branches let you change configurations, run them, and review a diff before merging anything into production.
“I want to explore data rather than build a pipeline.” Do ad-hoc analysis in a Python or R workspace, or query Storage directly from a SQL workspace.
“Other people need this data.” Publish it to the Data Catalog so other projects in your organization can link it read-only instead of copying it.
“It needs to be reliable.” Set up notifications, read Jobs when something fails, and follow the best practices cheat sheet for naming and structure before the project grows.
“I am rolling this out to a team.” Start with the platform onboarding guides: a usage blueprint, project architecture, a business data model, and governance.
Drive Keboola with an agent
Section titled “Drive Keboola with an agent”Everything you just did by clicking can be done by an AI assistant instead — the platform exposes the same operations three ways, and which one you pick depends on where you work.
- Kai — the assistant built into the project. It already knows your configurations, table schemas, and job logs, so it is the fastest way to ask “why did this fail?” or “write me the transformation that joins these tables.” No setup; currently in Public Beta.
- MCP server — connect Keboola to an AI assistant or IDE that speaks the Model Context Protocol (Claude, Cursor, and others). Use this when you want your coding agent to read and change the project while you work on code.
- kbagent CLI — Keboola from the terminal: projects, configurations, jobs, storage, and flows, scriptable, and usable by a coding agent that has a shell. In beta, so commands and output formats may still change. Start with the CLI quickstart.
If you are documenting or automating your own work on top of Keboola, the API reference is the layer underneath all three.
When you get stuck
Section titled “When you get stuck”- Support — the support form inside your project reaches us with the project context attached.
- keboolastatus.com — platform status, worth subscribing to.
- changelog.keboola.com — what shipped recently.
Back to: Getting Started →