Where to Go Next

You had a question: how close to your coast does an octopus live, and how deep down do they really go. It is answered, and it stays answered — the pipeline rebuilds the sheet every morning whether or not anyone thinks to ask again, and the map answers anyone who opens it. That is the whole shape of working in Keboola; everything else is a variation on it. Step 7, the last, of the Getting Started arc.

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
Data app	the map anyone can open, with the “how close to me?” field	Apps

Every job that ran is in Jobs, and every table it touched is in Storage.

Next time: one prompt

You built this arc one step at a time so every result stayed checkable. Now that you have seen the pieces, you no longer need five prompts — in a fresh project, the whole thing is one request:

Load the four CSVs from https://help.keboola.com/getting-started/ — occurrences.csv,
species.csv, depth_zones.csv and basins.csv — into Storage. Join them into one octopus_atlas
table with display names, depth zones and ocean basins. Deliver it to a Google Sheet, wire
loading, joining and delivering into a flow that runs daily, and build me a map app of all the
sightings with a nearest-sighting search by coordinates.

Kai will still ask you to approve what it creates, and the Google authorization stays yours — but that is the entire difference between one sentence and this guide. Swap the file URLs for your own data and the same sentence builds your pipeline instead.

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.

“The connector I need does not exist.” Build it. The Component Quick Start walks writing and packaging one, and joining or creating a vendor comes with access to a development Keboola project to test it in. Making it available to everyone else is a separate, approval-gated step: publishing.

“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

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.

• Kaithe assistant built into the project, and the one you have been using through this guide’s Do it with Kai tabs. 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. Use cases covers what it does beyond the arc — dashboards, project documentation, table descriptions, cohort analysis.
• MCP serverconnect 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 CLIKeboola 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

• 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 →