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, Dates, Value Types
• Facts: PnL
• Transformations: PnL Rollup, PnL Metrics, PnL Pivot

How to Use

1. Run the following command from your terminal or clone or fork the GitHub repository.

npx omoralabs@latest add plan_vs_actuals

2. Replace the CSVs with your own data.
3. Create a local or remote database and load sample data:

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

  # Create a remote database and load sample data
  uv run omora-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 date

# 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')