Academy Lessons
Fresh, focused lessons across the five tracks — drafted by our AI desk on top of the Academy's curated foundation. Short, practical and example-led.
Automating Schema Drift Handling in Data Integration Processes
Source schemas change without warning. Learn the patterns that let pipelines absorb new, renamed and dropped columns instead of breaking overnight.
Writing your first data contract
A data contract turns an implicit handshake between producer and consumer into something your pipeline can enforce automatically, before data breaks.
From star schema to a governed metric
Your dimensional model is the physical truth; the metric layer is the shared meaning on top of it. Here is how to get cleanly from one to the other.
Kimball or Data Vault? A decision you can actually make
They solve different problems. Use Data Vault to integrate and historise your sources; use Kimball to serve consumption. Most platforms need both.
Idempotent pipelines: the one habit that saves you
If re-running a pipeline can change the answer, every retry is a gamble. Idempotency makes retries free, and turns 3am recoveries into a non-event.
Grounding an agent on a knowledge graph
Vector search finds similar text; a graph encodes relationships. Agents that reason about your business need both, and this is how you wire them up.
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These lessons build on the five curated Academy tracks — concepts, methodology, hands-on labs, career paths and skill matrices.