Clari Reference Architecture

Clari Reference Architecture

Overview

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

Prerequisites

• Approved Clari export scope and a named revenue-data owner
• Separate development, staging, and production storage/state boundaries
• Warehouse access controls, retention policy, and audit logging
• An orchestrator capable of idempotent exports and reviewed recovery

Instructions

Build the pipeline in a non-production boundary first: pin the client and schema version, export a designated period, validate the manifest, and load through an idempotent warehouse operation. Promote the same reviewed design only after access controls, freshness alerts, and recovery behavior pass; keep individual forecast and owner data out of shared dashboards by default.

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

Error Handling

Output

Produce an architecture decision and pipeline manifest covering environment boundaries, owners, schema/client pins, data classification, retention, monitoring, recovery, and the latest certified export. Design diagrams are guidance only; the reviewed implementation and observed run evidence are the source of operational truth.

Examples

Deploy the daily export into staging with a separate warehouse role, verify that a repeated run does not duplicate rows, and test a delayed-export alert. Promote only the approved equivalent configuration to production; if a report contains unauthorized rep-level detail, restrict it and correct the access model before publishing another refresh.

Resources

• Clari Developer Portal
• Clari API Reference
• Snowflake Documentation

Next Steps

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