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Dbt Transformation Patterns

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Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

Originally by wshobson · MIT

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Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

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dbt-transformation-patterns/SKILL.md115 lines3.2 KBRawView on GitHub
Frontmatter — 2 properties
namedbt-transformation-patterns
descriptionMaster dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
1---
2name: dbt-transformation-patterns
3description: Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
4---A5 — No allowed-tools declared — no way to tell what this skill may touch
5 
6# dbt Transformation Patterns
7 
8Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.
9 
10## When to Use This Skill
11 
12- Building data transformation pipelines with dbt
13- Organizing models into staging, intermediate, and marts layers
14- Implementing data quality tests
15- Creating incremental models for large datasets
16- Documenting data models and lineage
17- Setting up dbt project structure
18 
19## Core Concepts
20 
21### 1. Model Layers (Medallion Architecture)
22 
23```
24sources/ Raw data definitions
25 ↓
26staging/ 1:1 with source, light cleaning
27 ↓
28intermediate/ Business logic, joins, aggregations
29 ↓
30marts/ Final analytics tables
31```
32 
33### 2. Naming Conventions
34 
35| Layer | Prefix | Example |
36| ------------ | -------------- | ----------------------------- |
37| Staging | `stg_` | `stg_stripe__payments` |
38| Intermediate | `int_` | `int_payments_pivoted` |
39| Marts | `dim_`, `fct_` | `dim_customers`, `fct_orders` |
40 
41## Quick Start
42 
43```yaml
44# dbt_project.yml
45name: "analytics"
46version: "1.0.0"
47profile: "analytics"
48 
49model-paths: ["models"]
50analysis-paths: ["analyses"]
51test-paths: ["tests"]
52seed-paths: ["seeds"]
53macro-paths: ["macros"]
54 
55vars:
56 start_date: "2020-01-01"
57 
58models:
59 analytics:
60 staging:
61 +materialized: view
62 +schema: staging
63 intermediate:
64 +materialized: ephemeral
65 marts:
66 +materialized: table
67 +schema: analytics
68```
69 
70```
71# Project structure
72models/
73├── staging/
74│ ├── stripe/
75│ │ ├── _stripe__sources.yml
76│ │ ├── _stripe__models.yml
77│ │ ├── stg_stripe__customers.sql
78│ │ └── stg_stripe__payments.sql
79│ └── shopify/
80│ ├── _shopify__sources.yml
81│ └── stg_shopify__orders.sql
82├── intermediate/
83│ └── finance/
84│ └── int_payments_pivoted.sql
85└── marts/
86 ├── core/
87 │ ├── _core__models.yml
88 │ ├── dim_customers.sql
89 │ └── fct_orders.sql
90 └── finance/
91 └── fct_revenue.sql
92```
93 
94## Detailed patterns and worked examples
95 
96Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.
97 
98## Best Practices
99 
100### Do's
101 
102- **Use staging layer** - Clean data once, use everywhere
103- **Test aggressively** - Not null, unique, relationships
104- **Document everything** - Column descriptions, model descriptions
105- **Use incremental** - For tables > 1M rows
106- **Version control** - dbt project in Git
107 
108### Don'ts
109 
110- **Don't skip staging** - Raw → mart is tech debt
111- **Don't hardcode dates** - Use `{{ var('start_date') }}`
112- **Don't repeat logic** - Extract to macros
113- **Don't test in prod** - Use dev target
114- **Don't ignore freshness** - Monitor source data
115 

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