clari-reference-architecture

name	clari-reference-architecture
description	Reference architecture for Clari revenue intelligence integrations. Use when designing a forecast data platform, planning Clari integration architecture, or establishing team patterns for revenue analytics. Trigger with phrases like "clari architecture", "clari data platform", "clari integration design", "clari best practices".
allowed-tools	Read, Write, Edit, Grep
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","revenue-intelligence","forecasting","clari"]
compatible-with	claude-code

Clari Reference Architecture

Overview

Production architecture for Clari revenue intelligence integrations: export pipeline design, data warehouse schema, analytics layer, and alerting.

Architecture Diagram

┌──────────────┐     ┌─────────────────┐     ┌──────────────────┐
│  Clari App   │     │  Clari Export    │     │  Data Warehouse  │
│  (SaaS)      │────▶│  API (v4)       │────▶│  (Snowflake/BQ)  │
└──────────────┘     └─────────────────┘     └────────┬─────────┘
                                                       │
                     ┌─────────────────┐     ┌────────▼─────────┐
                     │  Change         │     │  Analytics /     │
                     │  Detection      │────▶│  Dashboard       │
                     └─────────────────┘     │  (Looker/Metabase)│
                            │                └──────────────────┘
                     ┌──────▼──────────┐
                     │  Alerts         │
                     │  (Slack/Email)  │
                     └─────────────────┘

Project Structure

clari-data-platform/
├── src/
│   ├── clari_client.py         # API client wrapper
│   ├── export_pipeline.py      # ETL pipeline
│   ├── change_detector.py      # Forecast change tracking
│   ├── models.py               # Data models
│   └── config.py               # Environment config
├── dags/
│   └── clari_export_dag.py     # Airflow DAG
├── sql/
│   ├── schema.sql              # Warehouse table definitions
│   ├── merge.sql               # Upsert logic
│   └── analytics/
│       ├── forecast_accuracy.sql
│       ├── pipeline_coverage.sql
│       └── rep_performance.sql
├── tests/
│   ├── fixtures/               # Sample API responses
│   ├── test_pipeline.py
│   └── test_change_detector.py
├── scripts/
│   ├── run_export.sh
│   └── validate_schema.py
└── monitoring/
    ├── alerts.yaml             # Alert rules
    └── dashboard.json          # Grafana/Looker config

Data Warehouse Schema

-- Core tables
CREATE TABLE clari_forecasts (
    id BIGINT GENERATED ALWAYS AS IDENTITY,
    owner_name VARCHAR NOT NULL,
    owner_email VARCHAR NOT NULL,
    forecast_amount DECIMAL(15,2),
    quota_amount DECIMAL(15,2),
    crm_total DECIMAL(,),
    crm_closed (,),
    adjustment_amount (,),
    time_period  ,
    forecast_name  ,
    exported_at  ,
     (owner_email, time_period, forecast_name, exported_at)
);


 clari_forecast_changes (
    id  GENERATED ALWAYS  ,
    owner_email  ,
    time_period  ,
    previous_amount (,),
    current_amount (,),
    change_pct (,),
    detected_at  
);


  v_forecast_accuracy 

    time_period,
    owner_name,
    forecast_amount,
    crm_closed  actual_closed,
    ROUND((  (forecast_amount  crm_closed)  (forecast_amount, ))  , )  accuracy_pct
 clari_forecasts
 exported_at  ( (exported_at)  clari_forecasts f2  f2.time_period  clari_forecasts.time_period);