Plan vs Actuals

Overview

This blueprint demonstrates a production-grade Plan vs Actuals variance analysis built using Omora Labs components. It shows how semantic layers, facts, and dbt transformations compose into a reusable financial reporting pipeline with hierarchical P&L rollups and variance metrics.

Assets

• Source code: GitHub
• Live report: Hex

Live Report

Dependencies

• Semantic Layers: GL Accounts, GL Accounts Types, Periods, Value Types
• Facts: PnL
• Transformations: PnL Rollup, PnL Metrics, PnL Pivot

How to Use

1. Clone or fork the GitHub repository
2. Replace the CSVs with your own data following the provided semantic contracts
3. Create a local or remote database and load sample data:

# Create a local database and load sample data
  uv run plan-db-local

  # Create a remote database and load sample data
  uv run plan-db-remote

4. Run dbt to materialize the analytics models:

cd src/plan_vs_actuals/plan_vs_actuals_dbt
uv run dbt run

5. Connect to your favorite BI tool directly to the analytics layer. Example consumption on BI Tool:

SELECT * from "plan_vs_actuals"."analytics"."pnl_pivot"
ORDER BY period

# Title adjustments
df = sql_plan_and_actuals
df['display_name'] = df['gl_account'].str.replace('_', ' ').str.replace('pct', '%').str.title()
df['display_name'] = df['display_name'].str.replace('Csm', 'CSM')
df['display_name'] = df['display_name'].str.replace('Csm ', 'CSM ')
df['display_name'] = df['display_name'].str.replace('Cogs', 'COGS ')
df['display_name'] = df['display_name'].str.replace('Ebitda', 'EBITDA')