Build a Clari forecast export pipeline to your data warehouse. Use when exporting forecast calls, quota data, and CRM totals from Clari to Snowflake, BigQuery, or a local database. Trigger with phrases like "clari forecast export", "clari data pipeline", "clari to snowflake", "clari to bigquery", "export clari data".
Primary workflow: build an automated pipeline that exports forecast submissions, quota, adjustments, and CRM data from Clari to your data warehouse. Supports Snowflake, BigQuery, and PostgreSQL as targets.
clari-install-auth and clari-sdk-patterns setuprequests and your DB driver# config.py
from dataclasses import dataclass
@dataclass
class ExportConfig:
forecast_name: str # From Clari forecast list
time_periods: list[str] # e.g., ["2026_Q1", "2025_Q4"]
export_types: list[str] = None
currency: str = "USD"
include_historical: bool = True
def __post_init__(self):
if self.export_types is None:
self.export_types = [
"forecast", # Submitted forecast call
"forecast_updated", # Updated forecast history
"quota", # Quota values
"adjustment", # Manager adjustments
"crm_total", # Total CRM pipeline
"crm_closed", # Closed-won CRM amounts
]
# export_pipeline.py
from clari_client import ClariClient
from config import ExportConfig
import json
from datetime import datetime
def run_export(config: ExportConfig) -> list[dict]:
client = ClariClient()
all_entries = []
for period in config.time_periods:
print(f"Exporting {config.forecast_name} for {period}...")
data = client.export_and_download(
forecast_name=config.forecast_name,
time_period=period,
)
entries = data.get("entries", [])
for entry in entries:
entry["_exported_at"] = datetime.utcnow().isoformat()
entry["_forecast_name"] = config.forecast_name
all_entries.extend(entries)
print(f" {len(entries)} records exported")
return all_entries
def transform_forecast_data(entries: list[dict]) -> dict:
total_forecast = sum(e.get("forecastAmount", 0) for e in entries)
total_quota = sum(e.get("quotaAmount", 0) for e in entries)
total_closed = sum(e.get("crmClosed", 0) for e in entries)
return {
"total_forecast": total_forecast,
"total_quota": total_quota,
"total_closed": total_closed,
"attainment_percent": (total_closed / total_quota * 100) if total_quota else 0,
"coverage_ratio": (total_forecast / total_quota) if total_quota else 0,
"rep_count": len(entries),
"reps": entries,
}
# load_snowflake.py
import snowflake.connector
def load_to_snowflake(entries: list[dict], table: str = "CLARI_FORECASTS"):
conn = snowflake.connector.connect(
account=os.environ["SNOWFLAKE_ACCOUNT"],
user=os.environ["SNOWFLAKE_USER"],
password=os.environ["SNOWFLAKE_PASSWORD"],
database="REVENUE_DATA",
schema="CLARI",
)
cursor = conn.cursor()
cursor.execute(f"""
CREATE TABLE IF NOT EXISTS {table} (
owner_name VARCHAR,
owner_email VARCHAR,
forecast_amount FLOAT,
quota_amount FLOAT,
crm_total FLOAT,
crm_closed FLOAT,
adjustment_amount FLOAT,
time_period VARCHAR,
exported_at TIMESTAMP,
forecast_name VARCHAR
)
""")
for entry in entries:
cursor.execute(f"""
INSERT INTO {table} VALUES (
%(ownerName)s, %(ownerEmail)s, %(forecastAmount)s,
%(quotaAmount)s, %(crmTotal)s, %(crmClosed)s,
%(adjustmentAmount)s, %(timePeriod)s,
%(_exported_at)s, %(_forecast_name)s
)
""", entry)
conn.commit()
print(f"Loaded {len(entries)} records to {table}")
# Run daily export
if __name__ == "__main__":
config = ExportConfig(
forecast_name="company_forecast",
time_periods=["2026_Q1"],
)
entries = run_export(config)
summary = transform_forecast_data(entries)
print(f"Pipeline complete: {summary['rep_count']} reps, "
f"${summary['total_forecast']:,.0f} forecast, "
f"{summary['attainment_percent']:.1f}% attainment")
load_to_snowflake(entries)
| Error | Cause | Solution |
|---|---|---|
| Empty entries | No submitted forecasts for period | Verify period has data in Clari UI |
| Job timeout | Large export | Increase max_poll_attempts |
| Snowflake auth error | Wrong credentials | Check env vars |
| Duplicate records | Re-run without dedup | Add upsert logic with MERGE |
For pipeline analytics and deal inspection, see clari-core-workflow-b.