🔎 What is dbt?

dbt is a SQL-first transformation framework that compiles your models into the right SQL for your warehouse and executes them there. It brings software engineering discipline to analytics work:

• Modularity: Break logic into reusable SQL models and reference them with ref().
• Testing: Ship data with confidence using built-in tests (e.g., not null, unique) and custom assertions.
• Documentation: Auto-generate docs and lineage graphs dbt docs generate
• Version Control: Treat transformations like code—pull requests, code review, CI.
• Portability: Same dbt code works across multiple warehouses with minimal changes.

How dbt fits your stack

Extract/load tools (e.g., Fivetran, Airbyte) land raw data in your warehouse. dbt transforms raw into trusted models. BI tools (e.g., Looker, Power BI) sit on top of those models.

• EL: Airbyte/Fivetran → Warehouse
• T: dbt models, tests, docs
• BI: Looker, Power BI, Tableau

🧩 The dbt “Flavors”: Core, Cloud (Platform), and Fusion Engine

dbt Core (Open Source)

• What it is: Free CLI tool you run locally or in your own CI runners.
• Ideal for: Practitioners who want full control and OSS flexibility.
• Considerations: You bring your own scheduler (cron, Airflow), secrets management, and infra.

dbt Platform (dbt Cloud)

• What it is: Fully managed service with browser IDE, jobs/scheduler, CI, and observability.
• Ideal for: Teams and orgs who want guardrails, governance, and less DevOps.
• Benefits: Faster onboarding, built-in job runs, environment management, alerting.

dbt Fusion Engine

• What it is: Next-gen engine to make builds faster, cheaper, and more secure.
• Benefits: Velocity (faster compiles/exec), Efficiency (lower warehouse cost via optimizations), Trust (stronger governance).
• Ideal for: Enterprises scaling complex projects and seeking cost/perf wins.

🧪 Quick Examples

1) A simple model using ref()

-- models/stg_orders.sql
select
  id as order_id,
  customer_id,
  order_date,
  status
from {{ source('raw', 'orders') }};

-- models/fct_orders.sql
select
  o.order_id,
  o.customer_id,
  date_trunc('day', o.order_date) as order_day,
  sum(oi.quantity * oi.unit_price) as order_revenue
from {{ ref('stg_orders') }} o
join {{ ref('stg_order_items') }} oi on oi.order_id = o.order_id
group by 1,2,3;

2) Basic tests in schema.yml

version: 2

models:
  - name: stg_orders
    columns:
      - name: order_id
        tests:
          - not_null
          - unique
      - name: customer_id
        tests:
          - not_null

  - name: fct_orders
    columns:
      - name: order_day
        tests:
          - not_null

3) Tiny macro to DRY up SQL

{# macros/date_dim.sql #}
{% macro to_day(ts_col) -%}
  date_trunc('day', {{ ts_col }})
{%- endmacro %}

-- usage in a model:
select
  {{ to_day('o.order_timestamp') }} as order_day
from {{ ref('stg_orders') }} o;

🏗️ Real-World Use Cases

• Marketing & Growth: Transform event streams into user funnels, cohorts, and LTV models; ensure email tables have valid, non-null addresses.
• Finance: Standardize transactions into facts/dimensions; reconcile daily balances with tests to catch anomalies early.
• Product Analytics: Build feature usage tables and activation metrics; snapshot user states to track changes over time.
• Data Quality Guardrails: Add not_null and unique tests across primary keys; alert on failures via CI or job notifications.

⚖️ dbt Core vs Cloud vs Fusion Engine

🚦 Getting Started Quickly

1. Install dbt Core: Follow the official guide for your adapter (e.g., Postgres, DuckDB, Snowflake, BigQuery). Installation docs.
2. Do the Quickstart: Pick a warehouse (DuckDB is great locally) and complete the dbt Quickstart to build/run your first models and tests.
3. Add Tests & Docs: Write a schema.yml, run dbt test, then dbt docs generate and dbt docs serve.
4. Adopt Best Practices: Organize your project using stg_* for staging, and dim_* & fct_* for marts. Keep models small, modular, and focused.
5. Scale with Cloud: If you want browser IDE, built-in scheduling, and CI, explore dbt Cloud.

❓ FAQs

Do I need Python for dbt?

dbt is SQL-first. You only need Python to install and run dbt Core locally. Your transformations are written in SQL with Jinja templating.

Can I run dbt on my laptop without a cloud warehouse?

Yes—use the DuckDB adapter for a fully local setup during learning and prototyping.

What about orchestration?

With Core, use cron, Airflow, or GitHub Actions. With Cloud, you get a built-in scheduler and job UI.

📚 Helpful Resources

• What is dbt?
• dbt Quickstarts
• dbt-core and DuckDB
• Testing in dbt
• Jinja & Macros
• dbt Learn (free courses)
• Awesome dbt (curated links)

✅ Key Takeaways

• dbt brings engineering discipline—tests, docs, modularity—to SQL transformations in your warehouse.
• Start with dbt Core to learn; move to dbt Cloud for team workflows and scheduling.
• Fusion Engine targets speed, cost efficiency, and governance at enterprise scale.
• Do the Quickstart, add tests, and keep models modular—you’ll see value fast.