Data Platform tutorials
Complete use cases, end to end, with the code included.
How the tutorials are organized
From streaming a Kafka topic to publishing a Superset dashboard, each tutorial here is self-contained, and most ship with the code and sample data they need. They are grouped below by where they sit in the data journey.
The tutorials assume a working project. If you do not have one yet, the Getting started guide builds a first end-to-end application, and these tutorials extend from there.
They fall into four groups.
Project setup and configuration covers the work around a project: building your own app, exporting and importing configuration, generating API keys, and versioning.
Data ingestion and transformation covers getting data in and reshaping it: Kafka, IoT fleets, the Python SDK, SQL, external datasets, and segmentation.
Analytics and BI covers reading data back out: Trino, PySpark, AI Endpoints, billing analysis, and Apache Superset.
Data export and management covers the edges: dated export folders, bucket role conditions, and custom event handling.
Start with the project checklist if you are setting up a new project and want the shape of the work before the detail.
Your project checklist→
The shape of the work before the detail.
Project setup & configuration→
Apps, configuration, API keys and versioning.
Data ingestion & transformation→
Get data in, then reshape it.
Analytics & BI→
Trino, PySpark, AI Endpoints and Superset.
Data export & management→
Exports, permissions and inbound events.
Go further
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