Open-source curation · tools and learning paths
Data and AI tools, and the path to learn them
If you know the name, search for it. If you are just starting, there are paths that tell you what to learn, in what order and with which tools — without expecting you to know the industry map.
We curate open tools · KioskoAI's editorial content is proprietary
I know what I need
Search the kiosk
Enter a name or a concept and go straight to its entry, or to the guide that explains it.
I don't know where to start
Get oriented
Choose foundations or a profile. Five questions give you a concrete path through the catalogue.
See the pathsGuided orientation
Start from where you are
You do not need to choose a profession up front. Start with foundations or explore a Data Engineer or Data Scientist path.
Five questions per role · one per core skill · level + suggested path at the end
Explore with intent
Two ways into the catalogueThe modern stack shows what makes KioskoAI different; the foundations give you the base to use it. The inventory stays closed — only the way in changes.
Explore the modern stack
Specialised tools with little clear material in Spanish: this is where curation adds the most value.
DuckDB
The analytical database that runs inside your process — no server.
Polars
DataFrames in Rust: fast, parallel and with lazy evaluation.
Dagster
Pipeline orchestration centered on the data (assets), not just on tasks.
Foundations to get started
The languages and habits that support every path through data.
Python
The lingua franca of the data stack: from scripts to pipelines and ML.
SQL
The universal language for asking questions of your data.
Git
The version control that underpins all reproducible data work.
Where it fits
By layer
What kind of thing it is
By kind
Understand the data journeyOpen +−
The data journey
The data journeyThe path data follows end to end — from entering the ecosystem to becoming a chart. Each tool in its place in the flow. Click to open its guide.
ingestion → storage → processing → analysis → visualization
01
Ingestion
Data enters the ecosystem, in real time or in batches.
02
Storage
Where data lives: databases, formats and tables.
03
Processing
Transform and combine data at scale.
Library / framework
Engine / DB
04
Query & analysis
Query, explore and understand the data.
05
Visualization
Turn data into charts and dashboards.
Library / framework
Platform
They span the whole flow
Cross-cutting layers
Orchestration
Platform
Machine Learning
Library / framework
Governance
Library / framework
Platform
Compare the profilesOpen +−
Data Engineer or Data Scientist?
Profiles and what they shareTwo specialties with a shared core. See what they have in common and where each diverges — and pick where to go next.
Data Engineer
→Move reliable data, on time and at scale.
Data Scientist
→Analyze, model and communicate with data.