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Home Page: https://strvcom.github.io/dbt-workshop
dbt Workshop
Home Page: https://strvcom.github.io/dbt-workshop
dbt_ml.customer_registration_predictio
-> dbt_marts.customer_registration_predictio
Meeting notes:
before ml
install docker
clone the repo
run docker-compose (download X GB of data / usb disk)
sql ide required
intro motivation - ML = predict registered customers
describe briefly jinja
dbt_project - make consistent dbt_demo / dbt_workshop
leave out/work into more detail why to use identifier
test source and dbt test itx
Testing notes:
Notes:
docker exec -w /project/dbt_demo/ -it dbt-demo-dbt-1 /bin/bash
models/staging - add note from core
models/marts/core - core data model with dimensions and facts
models/marts/intermediate - combine two data sources form staging
run dbt debug
you should see All checks passed!
run dbt list
you should see
There are 3 unused configuration paths:
- models.dbt_workshop.staging
- models.dbt_workshop.marts
- models.dbt_workshop.marts.core
name = jaffle_shop
dbt seeds under dbt schema not dbt_seeds
add _src_ga schema: dbt
I would enrich by number of orders so it's clear that staging should be creating join on top of base models
we have created on src, not stg instead of stg
add subfolder where to put it
run docs command?
split ga and jaffle
add stg ga
docker cmd to copy docker cp update.sql dbt-demo-database-1:/update.sql
instead of docker cp update.sql dbt-workshop-database-1:/update.sql
labels into seeds
change base, stg, dim
create _ml.yml and _src_ml.yml
run fal ai
change dim_customers to contain flag
Create small exercise for using dbt python model on the given dataset
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