Awareness
Data Engineering vs Data Science vs Machine Learning: Who Does What
Three disciplines, constantly confused in job specs. Responsibilities, deliverables, tools, hiring order and the team structures that actually ship.
9 min read
Topic cluster
The platform work that decides whether your AI programme ever reaches production.
Pillar guide
The pillar guide to data platforms: reference architecture, build sequence, governance model, cost control and the anti-patterns that produce expensive shelfware.
Read the guideAwareness
Three disciplines, constantly confused in job specs. Responsibilities, deliverables, tools, hiring order and the team structures that actually ship.
9 min read
Consideration
The unglamorous work behind good AI answers: inventory, permission-aware retrieval, chunking strategy, freshness and the metrics that prove readiness.
12 min read
Awareness
Six pipeline patterns, when each applies, and the reliability practices (idempotency, backfills, contracts, observability) that keep them from waking you at 3am.
11 min read
Awareness
Every stalled AI programme has the same root cause. Data engineering explained in business terms, with a maturity test you can run this week.
10 min read
Next step
A ninety-minute session with the engineers who would build it. You leave with an architecture opinion and a costed next step.
The engineering notes
The findings from the apps we audit, the fixes that worked, and what each one cost, written by the person who did the work. No roundups, no reposts.
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