Paul Trowbridge
dcac6def87
- Add get_all_values() SQL function returning all extracted values (mapped + unmapped) with real record counts and mapping output in one query - Add /mappings/source/:source/all-values API endpoint - Rewrite All tab to use get_all_values directly instead of merging two separate API calls; counts now populated for all rows - Rewrite export.tsv to use get_all_values (real counts for mapped rows) - Fix save bug where editing one output field blanked unedited fields by merging drafts over existing mapping output instead of replacing - Add dirty row highlighting (blue tint) and per-rule Save All button - Fix sort instability during editing by sorting on committed values only Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> |
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| api | ||
| database | ||
| examples | ||
| scripts | ||
| ui | ||
| .env.example | ||
| .gitignore | ||
| CLAUDE.md | ||
| package.json | ||
| README.md | ||
| setup.sh | ||
| uninstall.sh | ||
Dataflow
A simple, understandable data transformation tool for ingesting, mapping, and transforming data from various sources.
What It Does
Dataflow helps you:
- Import data from CSV files (or other formats)
- Transform data using regex rules to extract meaningful information
- Map extracted values to standardized output
- Query the transformed data
Perfect for cleaning up messy data like bank transactions, product lists, or any repetitive data that needs normalization.
Core Concepts
1. Sources
Define where data comes from and how to deduplicate it.
Example: Bank transactions deduplicated by date + amount + description
2. Rules
Extract information using regex patterns.
Example: Extract merchant name from transaction description
3. Mappings
Map extracted values to clean, standardized output.
Example: "DISCOUNT DRUG MART 32" → {"vendor": "Discount Drug Mart", "category": "Healthcare"}
Architecture
- Database: PostgreSQL with JSONB for flexibility
- API: Node.js/Express for REST endpoints
- Storage: Raw data preserved, transformations are computed and stored
Design Principles
- Simple & Clear - Easy to understand what's happening
- Explicit - No hidden magic or complex triggers
- Testable - Every function can be tested independently
- Flexible - Handle varying data formats without schema changes
Getting Started
Prerequisites
- PostgreSQL 12+
- Node.js 16+
Installation
- Install dependencies:
npm install
- Configure database (copy .env.example to .env and edit):
cp .env.example .env
- Deploy database schema:
psql -U postgres -d dataflow -f database/schema.sql
psql -U postgres -d dataflow -f database/functions.sql
- Start the API server:
npm start
Quick Example
// 1. Define a source
POST /api/sources
{
"name": "bank_transactions",
"dedup_fields": ["date", "amount", "description"]
}
// 2. Create a transformation rule
POST /api/sources/bank_transactions/rules
{
"name": "extract_merchant",
"pattern": "^([A-Z][A-Z ]+)",
"field": "description"
}
// 3. Import data
POST /api/sources/bank_transactions/import
[CSV file upload]
// 4. Query transformed data
GET /api/sources/bank_transactions/records
Project Structure
dataflow/
├── database/ # PostgreSQL schema and functions
│ ├── schema.sql # Table definitions
│ └── functions.sql # Import and transformation functions
├── api/ # Express REST API
│ ├── server.js # Main server
│ └── routes/ # API route handlers
├── examples/ # Sample data and use cases
└── docs/ # Additional documentation
Status
Current Phase: Initial development - building core functionality
License
MIT