SEMMOD.SEM-06 · v1.0
Pipelines for
real workloads,
not demos.
6 micro-lessons · ~27 min · Real Docker images
PATCH MATRIX · LIVE
PHYS × SEM · 5 × 5
MATRIX.A · 11 PATCHES
revenue
mau
dau
cohort
funnel
orders
users
events
products
sessions
physical → semantic mapping · dbt + Cube
LIVE · 11/25
SEMDATA ENGINEERINGNEW
Semantic & context layers
One semantic layer your apps and AI agents both speak.
WHY THIS MATTERS · GARTNER 2030 FORECAST
Universal semantic layers will be treated as critical infrastructure by 2030. Context, semantics, and metadata are mission-critical for AI agents.
01Why semantic layers
02dbt + metric layer
03Cube.dev patterns
04Context for AI agents
05Universal vs domain semantics
Pick a metric layer for your stack
Expose semantics to AI agents safely
Bridge dbt → Cube → product
$ docker pull snap/semantic-context:lesson-01
$ docker run --rm -it snap/semantic-context:lesson-01
snap/semantic-context:lesson-01
Now I understand why agents kept hallucinating metrics.
@semantics_siVERIFY ON GITHUB
Bridging dbt → agent: clean lesson, runnable demo.
@devops_julesVERIFY ON GITHUB
LESSONS6
HOURS~0.45
LEARNERS640
THIS WEEK+38%