task-master
AI-powered task management for structured, specification-driven development. Use this skill when you need to manage complex projects with PR
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AI-powered task management for structured, specification-driven development. Use this skill when you need to manage complex projects with PRDs, break down tasks into subtasks, track dependencies, and
Details
- Kind
- Agent skills
- Topic
- Developer tools
- Publisher
- sfc-gh-dflippo
- Origin
- majiayu
- Category
- ferramentas
- Stars
- 23
- Forks
- 13
- Open pull requests
- 1
- Last push
- 2026-08-14T17:37:07Z
- Repository state
- ativo
- Language
- Python
- License
- Apache-2.0
- Added
- 2026-08-30 15:38:49
- Updated
- 2026-08-30 15:38:49
- Origin id
sfc-gh-dflippo/snowflake-dbt-demo/.claude/skills/task-master@main
README
# dbt for Snowflake Demonstration Project
## 📚 Documentation Navigation
| Document | Description |
| -------------------------------------------------- | ------------------------------------------------------------------------ |
| **[README.md](README.md)** | 👈 **You are here** - Project overview, architecture, and feature matrix |
| **[DBT_SETUP_GUIDE.md](DBT_SETUP_GUIDE.md)** | Complete installation and setup instructions for all platforms |
| **[DBT_BEST_PRACTICES.md](DBT_BEST_PRACTICES.md)** | Implementation guide for dbt modeling best practices |
---
## 🚀 Quick Start
**New to dbt?** Check out our comprehensive **[dbt Setup Guide](DBT_SETUP_GUIDE.md)** for detailed installation instructions across all platforms.
**Ready to explore?** This project demonstrates dbt best practices integrated with medallion architecture for comprehensive training.
### Prerequisites
- dbt 2.0 (Fusion) — install: `curl -fsSL https://public.cdn.getdbt.com/fs/install/install.sh | sh -s -- --update`
- SNOWFLAKE_SAMPLE_DATA (available by default in all Snowflake accounts)
- [Snowflake Finance & Economics](https://app.snowflake.com/marketplace/data-products/search?search=Finance%20%26%20Economics) (free in Snowflake Data Marketplace)
### Quick Setup
```bash
# Install dependencies
dbt deps
# Build the project
dbt build --full-refresh
```
For detailed setup instructions, see **[DBT_SETUP_GUIDE.md](DBT_SETUP_GUIDE.md)**.
### 📋 Quick Command Reference
```bash
# Essential commands
dbt deps # Install packages
dbt build # Run all models and tests
dbt build --full-refresh # Full reload of incremental models
# Selection commands
dbt build --select modelname # Run specific model
dbt build --select +modelname # Run model and parents
dbt build --select modelname+ # Run model and children
# Documentation
dbt docs generate # Generate documentation
dbt docs serve # Serve documentation locally
```
See **[DBT_SETUP_GUIDE.md](DBT_SETUP_GUIDE.md)** for complete command reference and troubleshooting.
## 🔍 Quick Feature Finder
Looking for a specific dbt feature? Jump directly to examples:
**🏗️ Materializations:** [Ephemeral](#materializations) • [Incremental](#materializations) • [Dynamic Tables](#materializations) • [Python Models](#materializations)
**🧪 Testing:** [dbt_constraints](#-testing-framework) • [Generic Tests](#-testing-framework) • [Singular Tests](#-testing-framework) • [Contracts](#-testing-framework)
**📊 Advanced:** [Snapshots](#-advanced-dbt-features) • [Exposures](#-advanced-dbt-features) • [Seeds](#-advanced-dbt-features) • [Analyses](#-advanced-dbt-features)
**🔧 Jinja:** [Advanced Templating](#-jinja--macros) • [Custom Macros](#-jinja--macros) • [Variables](#-jinja--macros) • [Loops](#-jinja--macros)
**⚙️ Snowflake:** [Streams](#snowflake-specific-features) • [Sequences](#snowflake-specific-features) • [Secure Views](#snowflake-specific-features) • [Warehouses](#snowflake-specific-features)
**📈 Business:** [Executive Dashboards](#-business-intelligence-integration) • [Customer Analytics](#-business-intelligence-integration) • [TPC-H Benchmarks](#-business-intelligence-integration)
---
## Project Architecture
This project demonstrates **dbt Best Practices integrated with Medallion Architecture** for comprehensive training:
### 🏆 Integrated Architecture: Best Practices + Medallion
- **Bronze Layer** (`models/bronze/`) - **Staging models** + raw data patterns
- **Silver Layer** (`models/silver/`) - **Intermediate models** + transformation examples
- **Gold Layer** (`models/gold/`) - **Marts models** + advanced analytics
### 📚 Complexity-Based Learning Path
Each layer contains three complexity levels with integrated best practices:
#### 🥉 **Bronze Layer: Staging + Raw Patterns**
- **Crawl** (`bronze/crawl/`) - Simple staging models with basic column renaming
- `stg_tpc_h__nations`, `stg_tpc_h__regions`, `stg_tpc_h__customers`, `stg_tpc_h__orders`
- **Walk** (`bronze/walk/`) - Complex staging with composite keys and external sources
- `stg_tpc_h__lineitem`, `stg_economic_essentials__fx_rates`, `stg_customers_with_tests`
- **Run** (`bronze/run/`) - Advanced staging with streams and incremental loading
- `customer_cdc_stream`, `stg_orders_incremental`
#### 🥈 **Silver Layer: Intermediate + Transformations**
- **Crawl** (`silver/crawl/`) - Simple data cleaning and standardization
- `clean_nations`
- **Walk** (`silver/walk/`) - Business logic and aggregations
- `int_customers__with_orders`, `customer_segments`, `lookup_exchange_rates`
- **Run** (`silver/run/`) - Complex transformations with advanced features
- `int_fx_rates__daily`, `order_facts_dynamic`, `customer_clustering`, `async_bulk_operations`
#### 🥇 **Gold Layer: Marts + Analytics**
- **Crawl** (`gold/crawl/`) - Simple aggregated views and summaries
- `dim_current_year_orders`, `dim_current_year_open_orders`, `nation_summary`
- **Walk** (`gold/walk/`) - Complete business dimensions and analytics
- `dim_customers`, `dim_orders`, `customer_insights`, `dim_calendar_day`, `dim_customer_changes`
- **Run** (`gold/run/`) - High-performance fact tables and advanced analytics
- `fct_order_lines`, `executive_dashboard`, `fact_order_line_full_reload`, `fact_order_line_pivot`
#### 🔧 **Other Layer: Utility + Reference Models**
- **Utility Models** (`other/`) - System monitoring and reference implementations
- `dbt_query_history`, `dynamic_warehouse_assignment`
- **Benchmarks** (`other/tpc_h_benchmarks/`) - Industry-standard performance queries
- `Q1_FACT_PRICING_SUMMARY_REPORT_QUERY`, `Q2_MINIMUM_COST_SUPPLIER_QUERY`, `Q3_SHIPPING_PRIORITY_QUERY`, `Q4_ORDER_PRIORITY_CHECKING_QUERY`
### 🎯 Best Practices Demonstrated
#### ✅ **Integrated Best Practices by Layer**
**🥉 Bronze (Staging) Best Practices:**
- **One-to-one source relationships**: Each source has exactly one staging model
- **Standardized naming**: `stg_{source_name}__{table_name}` convention
- **View materialization**: All staging models as views for optimal performance
- **Feature organization**: `basic_staging/` (crawl) vs `complex_staging/` (walk)
- **Complexity assessment**: Most staging is crawl-level (basic column renaming)
- **Comprehensive testing**: Primary keys, foreign keys, data quality checks
**🥈 Silver (Intermediate) Best Practices:**
- **Business logic isolation**: Complex transformations separated from staging
- **Feature organization**: `business_logic/` vs `advanced_transformations/`
- **Ephemeral materialization**: For reusable components (`business_logic/`)
- **Table materialization**: For complex logic that needs persistence (`advanced_transformations/`)
- **No direct source references**: Only references staging models
**🥇 Gold (Marts) Best Practices:**
- **Business-ready data**: Final data products for consumption
- **Feature organization**: `dimensions/` vs `facts/` by data modeling pattern
- **Proper dependencies**: References staging, intermediate, and other marts only
- **Clear naming**: `dim_` for dimensions, `fct_` for facts
- **Incremental materialization**: Large fact tables for scalability
- **Comprehensive business testing**: Relationship validation, business rule checks
**Key Rules Implemented:**
- ✅ **No Direct Joins to Source**: All models reference staging layer, not sources directly
- ✅ **Proper Staging Layer**: One-to-one relationship between sources and staging models
- ✅ **No Source Fanout**: Each source referenced by exactly one staging model
- ✅ **Proper Model Dependencies**: Clear lineage from staging → intermediate → marts
- ✅ **Standardized Naming**: Consistent `stg_`, `int_`, `dim_`, `fct_` prefixes
- ✅ **No Hard-coded References**: All references use `ref()` and `source()` functions
## 🎯 Comprehensive dbt Features Demonstrated
### **🏗️ Materializations** {#materializations}
| Feature | Model | Location | Layer | Description |
| ---------------------------- | ----------------------------- | -------------------------------------------------------------------------------------------------------------- | --------- | ---------------------------------------------------- |
| **Ephemeral (Staging)** | `stg_tpc_h__customers` | [`bronze/crawl/`](models/bronze/crawl/stg_tpc_h__customers.sql) | 🥉 Bronze | One-to-one source relationship, CTE compilation |
| **Ephemeral (Staging)** | `stg_tpc_h__lineitem` | [`bronze/walk/`](models/bronze/walk/stg_tpc_h__lineitem.sql) | 🥉 Bronze | Complex staging with composite keys |
| **Ephemeral (Intermediate)** | `int_customers__with_orders` | [`silver/walk/`](models/silver/walk/int_customers__with_orders.sql) | 🥈 Silver | Reusable business logic, aggregations |
| **Table (Intermediate)** | `int_fx_rates__daily` | [`silver/run/`](models/silver/run/int_fx_rates__daily.sql) | 🥈 Silver | Complex transformations with window functions |
| **Table (Dimensions)** | `dim_customers` | [`gold/walk/`](models/gold/walk/dim_customers.sql) | 🥇 Gold | Business-ready customer dimension |
| **Table (Dimensions)** | `dim_orders` | [`gold/walk/`](models/gold/walk/dim_orders.sql) | 🥇 Gold | Business-ready orders dimension |
| **Incremental (Facts)** | `fct_order_lines` | [`gold/run/`](models/gold/run/fct_order_lines.sql) | 🥇 Gold | High-performance fact table with incremental loading |
| **Incremental (Advanced)** | `stg_orders_incremental` | [`bronze/run/`](models/bronze/run/stg_orders_incremental.sql) | 🥉 Bronze | Advanced incremental with complex Jinja logic |
| **Incremental (Macros)** | `dim_customers_macro_example` | [`gold/run/incremental_with_macros/`](models/gold/run/incremental_with_macros/dim_customers_macro_example.sql) | 🥇 Gold | SCD Type 2 with custom macros |
| **Dynamic Table** | `order_facts_dynamic` | [`silver/run/`](models/silver/run/order_facts_dynamic.sql) | 🥈 Silver | Real-time analytics with dynamic tables |
| **Python Model (ML)** | `customer_clustering` | [`silver/run/`](models/silver/run/customer_clustering.py) | 🥈 Silver | Machine learning with scikit-learn |
| **Python Model (Async)** | `async_bulk_operations` | [`silver/run/`](models/silver/run/async_bulk_operations.py) | 🥈 Silver | Parallel processing and bulk operations |
| **View (Legacy)** | `dim_calendar_day` | [`gold/walk/`](models/gold/walk/dim_calendar_day.sql) | 🥇 Gold | Calendar dimension with ghost keys |
### **🧪 Testing Framework**
| Feature | Model/Location | Complexity | Description |
| ------------------------ | ------------------------------------ | -------------------------------------------------------------------------- | ---------------------------------------------------- | ----------------------------------------- |
| **dbt_constraints (PK)** | All dimension models | All | Primary key constraints with database enforcement |
| **dbt_constraints (FK)** | All fact models | All | Foreign key relationships with referential integrity |
| **dbt_constraints (UK)** | Various models | All | Unique key constraints for business rules |
| **Generic Tests** | `test_positive_values` | [`tests/generic/`](tests/generic/test_positive_values.sql) | Walk | Custom reusable test for positive values |
| **Singular Tests** | `test_customer_balance_distribution` | [`tests/singular/`](tests/singular/test_customer_balance_distribution.sql) | Run | Specific business rule validation |
| **Advanced Testing** | `fct_order_lines` | [`gold/_models.yml`](models/gold/_models.yml) | Run | Complex test combinations and expressions |
| **Contract Enforcement** | `order_facts_dynamic` | [`silver/_models.yml`](models/silver/_models.yml) | Run | Column contracts and data types |
| **dbt_utils Tests** | Various models | All | Unique combinations, accepted values, ranges |
### **📊 Advanced dbt Features**
| Feature | Model/Location | Complexity | Description |
| -------------------------- | -------------------------------- | --------------------------------------------------------------------------------------- | ----------- | -------------------------------------------- |
| **Snapshots (SCD Type 2)** | `DIM_CUSTOMERS_SCD` | [`snapshots/30_presentation/`](snapshots/30_presentation/DIM_CUSTOMERS_SCD.sql) | Run | Historical data tracking with check strategy |
| **Snapshots (Streams)** | `DIM_CUSTOMERS_FROM_STREAM` | [`snapshots/30_presentation/`](snapshots/30_presentation/DIM_CUSTOMERS_FROM_STREAM.sql) | Run | Change data capture with Snowflake streams |
| **Exposures** | `executive_dashboard_exposure` | [`exposures/`](exposures/executive_dashboard_exposure.yml) | Walk | BI tool integration and lineage |
| **Analyses** | `customer_cohort_analysis` | [`analyses/`](analyses/customer_cohort_analysis.sql) | Run | Exploratory data analysis |
| **Seeds** | `dynamic_warehouses` | [`seeds/`](seeds/dynamic_warehouses.csv) | Walk | Reference data management |
| **Operations** | `medallion_architecture_helpers` | [`macros/`](macros/medallion_architecture_helpers.sql) | Run | Custom dbt operations |
### **🔧 Jinja & Macros**
| Feature | Model/Location | Complexity | Description |
| ------------------- | --------------------------- | ------------------------------------------------------------- | ----------- | ------------------------------------------ |
| **Advanced Jinja** | `order_facts_dynamic` | [`silver/run/`](models/silver/run/order_facts_dynamic.sql) | Run | Complex loops, conditionals, and variables |
| **Custom Macros** | `snowflake_integration_key` | [`macros/`](macros/snowflake_integration_key.sql) | Run | Surrogate key generation |
| **Custom Macros** | `insert_ghost_key` | [`macros/`](macros/insert_ghost_key.sql) | Run | Ghost key insertion for dimensions |
| **Custom Macros** | `get_scd_sql` | [`macros/`](macros/get_scd_sql.sql) | Run | SCD Type 2 SQL generation |
| **Jinja Variables** | `raw_orders_incremental` | [`bronze/run/`](models/bronze/run/raw_orders_incremental.sql) | Run | Dynamic SQL with environment variables |
| **Jinja Loops** | `fact_order_line_pivot` | [`gold/run/`](models/gold/run/fact_order_line_pivot.sql) | Run | Dynamic column generation |
### **⚙️ Snowflake-Specific Features** {#snowflake-specific-features}
| Feature | Model/Location | Complexity | Description |
| -------------------- | ------------------------ | -------------------------------------------------------------------------------------- | ------------------------------------ | ------------------------------------- |
| **Dynamic Tables** | `order_facts_dynamic` | [`silver/run/`](models/silver/run/order_facts_dynamic.sql) | Run | Real-time materialized views with lag |
| **Streams** | `customer_cdc_stream` | [`bronze/run/`](models/bronze/run/customer_cdc_stream.sql) | Run | Change data capture streams |
| **Sequences** | Various dimension models | Run | Auto-incrementing surrogate keys |
| **Secure Views** | `DIM_CUSTOMERS_SHARE` | [`gold/walk/sensitive_data/`](models/gold/walk/sensitive_data/DIM_CUSTOMERS_SHARE.sql) | Walk | Data sharing with row-level security |
| **Transient Tables** | Various models | All | Optimized storage for temporary data |
| **Query Tags** | All models | All | Query identification and monitoring |
| **Warehouses** | Various models | All | Dynamic warehouse assignment |
### **🔄 Development Workflow**
| Feature | Location | Complexity | Description |
| --------------------- | ----------------------- | ---------- | ----------------------------------------------------------------------- |
| **Project Structure** | `dbt_project.yml` | All | Medallion architecture configuration |
| **Variables** | `dbt_project.yml` | Walk | Global configuration management |
| **Environments** | `profiles.yml.sample` | Walk | Multi-environment setup |
| **Packages** | `packages.yml` | All | External package management (dbt_constraints, dbt_utils, dbt_artifacts) |
| **Documentation** | `DBT_SETUP_GUIDE.md` | All | Complete installation and configuration guide |
| **Best Practices** | `DBT_BEST_PRACTICES.md` | All | Implementation guide for dbt modeling standards |
| **Hooks (Pre/Post)** | Various models | Run | Custom SQL execution before/after model runs |
| **Grants** | `dbt_project.yml` | All | Automated permission management |
### **📈 Business Intelligence Integration**
| Feature | Model/Location | Complexity | Description |
| -------------------------- | ------------------------- | --------------------------------------------------------------------------------------------------- | ----------- | ----------------------------------------- |
| **Executive Dashboard** | `executive_dashboard` | [`gold/run/`](models/gold/run/executive_dashboard.sql) | Run | KPI aggregations for leadership reporting |
| **Customer Insights** | `customer_insights` | [`gold/walk/`](models/gold/walk/customer_insights.sql) | Walk | Customer analytics and segmentation |
| **TPC-H Performance** | `Q1_FACT_PRICING_SUMMARY` | [`other/tpc_h_benchmarks/`](models/other/tpc_h_benchmarks/Q1_FACT_PRICING_SUMMARY_REPORT_QUERY.sql) | Run | Industry-standard performance benchmarks |
| **Query History Analysis** | `dbt_query_history` | [`other/`](models/other/dbt_query_history.sql) | Run | dbt execution monitoring and optimization |
### **🎓 Training & Learning Path**
| Complexity | Focus | Example Models | Key Concepts |
| -------------------------- | ------------------------------------------- | --------------------------------------------- | -------------------------------------------------- |
| **🥉 Crawl (Beginner)** | Basic staging, simple transformations | `stg_tpc_h__customers`, `raw_nations` | Source relationships, basic SQL, ephemeral models |
| **🥈 Walk (Intermediate)** | Business logic, intermediate models | `int_customers__with_orders`, `dim_customers` | Aggregations, joins, business rules, testing |
| **🥇 Run (Advanced)** | Complex analytics, performance optimization | `fct_order_lines`, `customer_clustering` | Incremental loading, Python models, advanced Jinja |
## Resources
- Learn more about dbt [in the docs](https://docs.getdbt.com/docs/introduction)
- Free [on-demand training](https://courses.getdbt.com/)
- [Additional Packages](https://hub.getdbt.com/)
- Create PK, UK, and FK in Snowflake using [dbt Constraints](https://github.com/Snowflake-Labs/dbt_constraints)
- Snowflake Guide - [Accelerating Data Teams with dbt Core & Snowflake](https://quickstarts.snowflake.com/guide/data_teams_with_dbt_core/index.html)
- Snowflake Guide - [Accelerating Data Teams with dbt Cloud & Snowflake](https://quickstarts.snowflake.com/guide/data_teams_with_dbt_cloud/index.html)
- Snowflake Guide - [Data Engineering with Apache Airflow, Snowflake & dbt](https://quickstarts.snowflake.com/guide/data_engineering_with_apache_airflow/index.html)