Paul Trowbridge
4cf5be52e8
Replaces the nested FOR loops (row-by-row, rule-by-rule) with a single SQL CTE chain that processes all records × rules in one pass, mirroring the TPS approach. CTE chain: qualifying → all untransformed records for the source rx → apply each rule (extract/replace) to each record linked → LEFT JOIN mappings to find mapped output rule_output → build per-rule JSONB (with retain support) record_additions → merge all rule outputs per record in sequence order UPDATE → set transformed = data || additions Also adds jsonb_concat_obj aggregate (jsonb merge with ORDER BY support) needed to collapse multiple rule outputs per record into one object. 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