clari-reference-architecture | clari-pack

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clari-reference-architecture | clari-pack

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(15,2),
    crm_closed DECIMAL(15,2),
    adjustment_amount DECIMAL(15,2),
    time_period VARCHAR NOT NULL,
    forecast_name VARCHAR NOT NULL,
    exported_at TIMESTAMP NOT NULL,
    PRIMARY KEY (owner_email, time_period, forecast_name, exported_at)
);

-- Change tracking
CREATE TABLE clari_forecast_changes (
    id BIGINT GENERATED ALWAYS AS IDENTITY,
    owner_email VARCHAR NOT NULL,
    time_period VARCHAR NOT NULL,
    previous_amount DECIMAL(15,2),
    current_amount DECIMAL(15,2),
    change_pct DECIMAL(5,2),
    detected_at TIMESTAMP NOT NULL
);

-- Analytics views
CREATE VIEW v_forecast_accuracy AS
SELECT
    time_period,
    owner_name,
    forecast_amount,
    crm_closed AS actual_closed,
    ROUND((1 - ABS(forecast_amount - crm_closed) / NULLIF(forecast_amount, 0)) * 100, 1) AS accuracy_pct
FROM clari_forecasts
WHERE exported_at = (SELECT MAX(exported_at) FROM clari_forecasts f2 WHERE f2.time_period = clari_forecasts.time_period);

Key Design Decisions

Decision	Choice	Rationale
Export frequency	Daily	Balances freshness vs API load
Data format	JSON export	Structured, easy to parse
Pipeline orchestration	Airflow	Retry, monitoring, DAG visualization
Change detection	Snapshot comparison	Clari has no real-time webhooks
Warehouse	Snowflake	SQL analytics, dbt compatibility

Resources

Next Steps

This completes the Clari skill pack. Start with clari-install-auth for new integrations.