MongoDB to PostgreSQL Migration Platform

Project Overview

A backend-focused migration platform for moving large and growing MongoDB datasets into PostgreSQL while preserving integrity and supporting schema mapping, validation, deduplication, warning handling, and final PostgreSQL export. The system is designed to handle datasets that grow continuously and must be migrated without data loss or duplication.

Problem

MongoDB source data can grow continuously and may not map directly to the PostgreSQL target schema. A direct import is not reliable — records may contain missing fields, unexpected types, or structural inconsistencies that break relational constraints. Beyond schema differences, duplicate records can accumulate across repeated source exports. The migration workflow needs to accommodate non-specialist users who require a clear, guided process rather than raw database tooling.

My Role

I was responsible for the backend architecture, migration workflow design, schema mapping logic, deduplication strategy, data validation layer, warning detection and resolution workflows, and the final PostgreSQL export design.

Technical Approach

• MongoDB dump and import into a staging environment for safe inspection
• Schema inspection to identify field types, nullability, and structural patterns
• Automatic schema mapping from MongoDB document structure to relational PostgreSQL tables
• Warning detection for fields that cannot be mapped automatically or contain unexpected values
• Manual warning resolution step for edge cases requiring human judgment
• Deduplication before insertion using identifier fields and fallback field combinations where a single unique key is not available
• Migration progress tracking so operators can monitor batch status
• PostgreSQL validation after insertion to confirm integrity
• Final export and handoff workflow

Core Architecture

The platform operates as a backend pipeline with discrete stages: source ingestion, schema analysis, transformation, deduplication, validation, and target insertion. Each stage is designed to be observable and recoverable — failed stages can be retried without reprocessing completed work. The deduplication layer uses configurable identifier combinations rather than assuming a single universal key, which makes it adaptable to different source dataset structures.

Key Engineering Challenge

The core engineering challenge is reliable deduplication across continuously growing datasets. Duplicate detection depends on identifiers and fallback field combinations because source data does not always contain a single guaranteed unique key. This requires a configurable deduplication strategy that can be tuned per dataset while still providing strong guarantees before insertion into PostgreSQL.

Technologies

Outcome / What the System Enables

The platform provides a structured, repeatable workflow for migrating MongoDB datasets into PostgreSQL without manual scripting for each migration. It surfaces warnings early, enables human resolution of edge cases, and enforces deduplication and validation before any data reaches the target database. The design supports future dataset growth by treating the migration as a pipeline rather than a one-time bulk operation.