production-level walkthrough

 Below is a deep, production-level walkthrough of each step in your dbt pipeline—from raw → staging → incremental → SCD → marts → testing → deployment—including:

• dbt internal components involved
• SQL / Python role
• Why the step exists
• How Git supports each step

STEP 1: RAW DATA EXTRACT (Sources)

🔹 What happens

• Data lands in Snowflake raw schema via:
  • COPY INTO / Snowpipe
• dbt does NOT ingest, but registers sources

🔹 dbt Internal Components

• Source Config (sources.yml)
• Parser
• Adapter (Snowflake adapter)

🔹 SQL / Python Role

SELECT * FROM {{ source('raw','customers_raw') }}

• SQL references raw tables
• Python (optional): external ingestion pipelines

🔹 Why required

• Establish trusted entry point
• Enables lineage tracking
• Prevents direct table coupling

🔹 Git Role

• Version control for:
  • sources.yml
• Tracks schema evolution
• Enables rollback if source changes break pipeline.

STEP 2: STAGING LAYER

🔹 What happens

• Clean, standardize, rename columns
• Apply light transformations

🔹 dbt Internal Components

• Model Parser
• Jinja Engine (macros)
• Compiler
• Execution Engine

🔹 SQL Example

SELECT

  customer_id,

  LOWER(email) AS email,

  TRIM(name) AS name

FROM {{ source('raw','customers_raw') }}

🔹 Why required

• Removes inconsistencies
• Creates reusable canonical layer

🔹 Git Role

• Tracks column renaming decisions
• Enables code review for transformations
• Collaboration across teams

STEP 3: INCREMENTAL MODELS

🔹 What happens

• Load only new or changed data
• Avoid full table rebuilds

🔹 dbt Internal Components

• Materialization Engine (incremental)
• Macro Engine (is_incremental())
• Adapter-specific SQL generation

🔹 SQL Example

SELECT *

FROM {{ ref('stg_customers') }}

{% if is_incremental() %}

WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})

{% endif %}

🔹 Why required

• Improves performance
• Reduces compute cost in Snowflake

🔹 Git Role

• Tracks logic changes for incremental filters
• Prevents accidental full refresh bugs
• Enables safe experimentation via branches

STEP 4: SCD TYPE 2 (HISTORICAL TRACKING)

🔹 What happens

• Tracks historical changes in dimensions
• Maintains:
  • surrogate keys
  • effective dates
  • current flag

🔹 dbt Internal Components

• Custom Macros (SCD logic)
• Incremental + MERGE strategy
• Dependency Graph (ref)

🔹 SQL Example (MERGE)

MERGE INTO dim_customers t

USING stg_customers s

ON t.customer_id = s.customer_id AND t.is_current = TRUE

WHEN MATCHED AND t.email <> s.email THEN

UPDATE SET is_current = FALSE, effective_end_date = CURRENT_TIMESTAMP

WHEN NOT MATCHED THEN

INSERT (...)

🔹 Why required

• Enables:
  • historical reporting
  • audit tracking
  • slowly changing dimensions

🔹 Git Role

• Critical for:
  • tracking SCD logic changes
  • auditing business rule evolution
• Enables peer review for complex logic

STEP 5: DATA MART (GOLD LAYER)

🔹 What happens

• Build fact and dimension tables
• Optimize for BI queries

🔹 dbt Internal Components

• Ref Graph (DAG builder)
• Materializations (table/view)
• Query Planner

🔹 SQL Example

SELECT

  o.order_id,

  c.customer_sk,

  o.order_amount

FROM {{ ref('fact_orders') }} o

JOIN {{ ref('dim_customers') }} c

🔹 Why required

• Serves:
  • dashboards
  • analytics
• Improves performance via denormalization

🔹 Git Role

• Tracks business logic
• Maintains consistency across metrics
• Enables versioned analytics definitions

STEP 6: TESTING & VALIDATION

🔹 What happens

• Validate data quality:
  • uniqueness
  • null checks
  • referential integrity

🔹 dbt Internal Components

• Test Runner
• Schema YAML parser
• Assertion engine

🔹 YAML Example

columns:

  - name: customer_id

    tests:

      - not_null

      - unique

🔹 Why required

• Prevents bad data propagation
• Ensures trust in analytics

🔹 Git Role

• Tracks test coverage
• Enforces quality via CI/CD

• Prevents bad merges

STEP 7: DOCUMENTATION & DEPLOYMENT

🔹 What happens

• Generate lineage docs
• Deploy models via jobs

🔹 dbt Internal Components

• Docs Generator
• Manifest.json
• Run Artifacts
• DAG Renderer

🔹 Commands

dbt docs generate

dbt docs serve

🔹 Why required

• Provides:
  • lineage graph
  • column-level documentation
• Helps onboarding & governance

🔹 Git Role

• Stores:
  • documentation YAML
• Enables:
• versioned lineage
• auditability

Internal Flow inside dbt:

1. Parser
   • Reads SQL + YAML
2. Jinja/Macro Engine
   • Expands dynamic logic
3. Compiler
   • Converts to raw SQL
4. Adapter (Snowflake)
   • Optimizes SQL
5. Execution Engine
   • Runs queries
6. Test Engine
   • Validates output
7. Docs Generator
• Builds lineage + metadata

RAW → SOURCE → STAGING → INCREMENTAL → SCD → MART → TEST → DOCS

🚀 Key Takeaways

• dbt is a compiler + orchestrator, not ingestion tool
• SQL is the primary transformation layer
• Python is optional (advanced models)
• Git enables:
  • collaboration
  • versioning
  • CI/CD
• Each step builds trust, performance, and scalability