Side-by-side comparison of total expenditures across overlapping years
Data Sources: budget_analysis database (2017-2025) vs nep database (2020-2026)
Note: Only overlapping years (2020-2025) are shown for direct comparison
Live Data: Chart updates automatically with real database values
Impact: Despite schema differences, the NEP integration is successful because adapted queries handle the column mapping differences transparently.
6 Years
2020-2025 shared between databases
8 Columns
Budget: 24 cols, NEP: 16 cols
Adapted
bigint โ varchar for UACS codes
Aspect | Budget Database | NEP Database | Adaptation |
---|---|---|---|
Primary Key | id (integer) |
id (integer) |
โ Identical |
Amount Field | amt (numeric) |
amount (numeric) |
โ Column name change |
Description | dsc (text) |
description (text) |
โ Column name change |
Fiscal Year | year (integer) |
fiscal_year (varchar) |
โ Name + type change |
Department Code | department (bigint) |
org_uacs_code (varchar) |
โ Name + type change |
Agency Code | agency (bigint) |
region_code (varchar) |
โ Name + type change |
Sort Order | sorder (bigint) |
sort_order (bigint) |
โ Column name change |
Total Columns | 24 columns | 16 columns | โ Schema reduction |
amt
โ amount
)
Conclusion: Despite fundamental schema differences, the NEP integration provides users with the same powerful analysis capabilities through transparent query adaptations.