DataPipe

The Problem

Every data project starts the same way: cleaning CSVs and Excel files. Empty fields, duplicate rows, type mismatches, inconsistent formatting - the same problems appear over and over. I wanted to build a tool that handles this automatically, so I could focus on analysis instead of data prep.

What I Built

DataPipe is a full-stack tool for importing CSV and Excel data into a local PostgreSQL database. It handles input sanitization, empty field cleanup, duplicate detection, and schema validation before writing clean records. The goal: reliable imports without manual cleanup.

Technical Implementation

Validation Layer

The validation layer is where the real value lives. It catches common data quality issues before they hit the database:

• Empty fields - flags required fields that are missing
• Duplicate rows - detects and prevents duplicate entries
• Type mismatches - catches data that doesn't match expected schema

Each issue is flagged with a clear explanation, so you know exactly what needs fixing.

Tech Stack

• Frontend: React
• Backend: Node.js, Express.js
• Database: PostgreSQL

Import Pipeline

The backend processes files in chunks to handle large datasets without memory issues. Schema validation happens in two stages:

1. Structural validation - column types, required fields
2. Content validation - business rules, data ranges

Why I Built This

I built this because I was tired of writing the same cleaning scripts for every project. Now DataPipe handles the tedious work, and I can trust that my data is clean before analysis begins.

Key Takeaway

Data quality is the foundation of reliable analysis. By catching issues at import time instead of discovery time, DataPipe prevents downstream problems and saves hours of debugging.