beanflows/transform/sqlmesh_materia/models

Data Engineering Pipeline Layers & Naming Conventions

This document outlines the standard layered architecture and model naming conventions for our data platform. Adhering to these standards is crucial for maintaining a clean, scalable, and understandable project.

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Data Pipeline Layers

Each layer has a distinct purpose, transforming data from its raw state into a curated, analysis-ready format.

1. Raw Layer

The initial landing zone for all data ingested from source systems.

• Purpose: To create a permanent, immutable archive of source data.
• Key Activities:
  • Data is ingested and stored in its original, unaltered format.
  • Serves as the definitive source of truth, enabling reprocessing of the entire pipeline if needed.
  • No transformations or schema enforcement occur at this stage.

2. Staging Layer

A workspace for initial data preparation and technical validation.

• Purpose: To convert raw data into a structured, technically sound format.
• Key Activities:
  • Schema Application: A schema is applied to the raw data.
  • Data Typing: Columns are cast to their correct data types (e.g., string to timestamp, integer to decimal).
  • Basic Cleansing: Handles technical errors like malformed records and standardizes null values.

3. Cleaned Layer

The integrated core of the data platform, designed to create a "single version of the facts."

• Purpose: To integrate data from various sources into a unified, consistent, and historically accurate model.
• Key Activities:
  • Business Logic: Complex business rules are applied to conform and validate the data.
  • Integration: Data from different sources is combined using business keys.
  • Core Modeling: Data is structured into a robust, integrated model (e.g., a Data Vault) that represents core business processes.

4. Serving Layer

The final, presentation-ready layer optimized for analytics, reporting, and business intelligence.

• Purpose: To provide high-performance, easy-to-query data for end-users.
• Key Activities:
  • Analytics Modeling: Data from the Cleaned Layer is transformed into user-friendly models, such as Fact and Dimension tables (star schemas).
  • Aggregation: Key business metrics and KPIs are pre-calculated to accelerate queries.
  • Consumption: This layer feeds dashboards, reports, and analytical tools. It is often loaded into a dedicated Data Warehouse for optimal performance.

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Model Naming Conventions

A consistent naming convention helps us understand a model's purpose at a glance.

Guiding Principles

1. Be Explicit: Names should clearly state the layer, source, and entity.
2. Be Consistent: Use the same patterns and abbreviations everywhere.
3. Use Prefixes: Start filenames and model names with the layer to group them logically.

Layer-by-Layer Naming Scheme

1. Raw / Sources Layer

This layer is for defining sources, not models. The convention is to name the source after the system it comes from.

• Source Name: [source_system] (e.g., salesforce, google_ads)
• Table Name: [original_table_name] (e.g., account, ads_performance)

2. Staging Layer

Staging models have a 1:1 relationship with a source table.

• Pattern: stg_[source_system]__[entity_name]
• Examples:
  • stg_stripe__charges.sql
  • stg_google_ads__campaigns.sql

3. Cleaned Layer

This is the integration layer for building unified business entities or a Data Vault.

• Pattern (Integrated Entity): cln_[entity_name]
• Pattern (Data Vault): cln_[vault_component]_[entity_name]
• Examples:
  • cln_customers.sql
  • cln_hub_customers.sql
  • cln_sat_customer_details.sql

4. Serving Layer

This layer contains business-friendly models for consumption.

• Pattern (Dimension): dim_[entity_name]
• Pattern (Fact): fct_[business_process]
• Pattern (Aggregate): agg_[aggregation_description]
• Examples:
  • dim_customers.sql
  • fct_orders.sql
  • agg_monthly_revenue_by_region.sql

Summary Table

Layer	Purpose	Filename / Model Name Example	Notes
Raw	Source Declaration	sources.yml (for stripe, charges)	No models, just declarations.
Staging	Basic Cleansing & Typing	stg_stripe__charges.sql	1:1 with source tables.
Cleaned	Integration & Core Models	cln_customers.sql or cln_hub_customers.sql	Integrates sources. Your Data Vault lives here.
Serving	Analytics & BI	dim_customers.sql or fct_orders.sql	Business-facing, optimized for queries.