# 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.       |
