dbt is wonderful and becomes a mess without discipline. The practices that keep large dbt projects healthy are knowable.
Layered Architecture
staging (source cleanup) → intermediate (business logic) → marts (consumption). Never mix layers.
Tests And Documentation
Unique, not_null, relationships tests on critical models. Docs in YAML.
Incremental Models
For tables over millions of rows. Set properly or you get incorrect data.
Meta And Tags
Owner, refresh cadence, business context. Searchable catalog beats tribal knowledge.
Who This Is For
- Data and analytics engineering leaders
- CTOs modernizing their data stack
- Teams making decisions off data they can't yet trust
Common Mistakes
- Buying the stack before defining what decisions it supports
- Ignoring data contracts until pipelines break at 3am
- Assuming dashboards equal data quality
Business Impact
- Single source of truth for every business metric
- Analytics velocity that matches product velocity
- Data systems that power AI without rewrites
Frequently Asked Questions
dbt Cloud or Core?
Core for flexibility, Cloud for team UX. Both viable.
Snapshots?
For slowly-changing dimensions. Use where business logic needs historical state.
Testing strategy?
Contract tests, data tests, unit tests (dbt-unit-testing). Pyramid applies here too.
Why AIM Tech AI
- Custom-built systems, not templates or off-the-shelf wrappers
- AI + backend + cloud + infrastructure expertise in one team
- Built for production scale, not demo-day experiments
- Beverly Hills, California — serving clients worldwide
Build Systems, Not Experiments
AIM Tech AI designs and ships AI, cloud, and custom software systems for companies ready to turn technology into real business advantage.
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