A large retail application's core database needed to move from Microsoft SQL Server to PostgreSQL on AWS, with a hard requirement of zero data loss and minimal downtime. The database ran the business itself: roughly 1TB of data, 250+ tables, and 700+ indexes under a continuous, high-volume transactional workload.

Business Challenges

The scale of the database and the zero-tolerance requirement for data loss ruled out a simple lift-and-shift approach.

Massive, high-stakes scale

1TB of production data, 250+ tables, and 700+ indexes needed to move without disrupting a live, high-volume transactional workload.

CDC pipeline instability

A Change Data Capture pipeline built for near-zero downtime broke down under real production load, with transaction log growth, replication lag, and backup interference.

Zero tolerance for data loss

The database ran the business, leaving no room for lost or corrupted rows during the migration.

Cross-platform differences

SQL Server to PostgreSQL migrations carry platform gaps in encoding, timestamps, case sensitivity, and data types with no clean mapping.

Project Objectives

After the original CDC-based architecture proved unstable in production, Geopits redesigned the migration around a full-load approach engineered for speed, predictability, and validation at every step.

Key Goals:

Move off SQL Server onto PostgreSQL with zero data loss
Keep downtime inside a tightly defined maintenance window
Validate every row, index, and object before and after cutover

Solution Provided by Geopits

When the initial CDC pipeline (SQL Server, CDC, Debezium, Kafka, PostgreSQL) showed instability under production traffic, Geopits pivoted to a full-load migration built for speed, thorough validation, and a safe rollback path.

Custom full-load framework

A custom Python migration framework, parallel extraction and loading, PostgreSQL's COPY command, and deferred index creation moved data at maximum speed.

Comprehensive validation

Row counts, schema, all 700+ indexes, and application-level functionality were validated throughout the project, not just at the end.

Rehearsed cutover

The full migration was rehearsed repeatedly against production backups in an isolated UAT environment until the process became predictable.

Reverse sync safety net

A temporary reverse synchronization pipeline was stood up after cutover, replicating changes back to SQL Server as a rollback path.

Results & Business Impact

Comparing the abandoned CDC approach against the full-load migration that ultimately shipped shows why the pivot was the right call.

Capabilities

Before

After

Downtime approach

Near-zero, continuous streaming

Planned maintenance window, minimized

Production impact

Transaction log growth, replication lag, backup interference

Isolated to a single controlled window

Data loss

Risk under sustained instability

Zero

Rollback path

Not built in

Reverse sync pipeline to SQL Server

Outcome

Abandoned after production testing

Successfully shipped

Key outcomes

Zero data loss

All 1TB of production data, across 250+ tables and 700+ indexes, moved with zero data loss.

Migration completed on schedule

The full-load migration was completed within the planned maintenance window.

Validated at every layer

Row counts, schema, indexes, and application functionality were all verified before and after cutover.

Safety net in place

A reverse sync pipeline back to SQL Server gave the team a rollback path that didn't depend on restoring from backups.

Conclusion

Geopits migrated a 1TB retail application database from SQL Server to PostgreSQL with zero data loss, pivoting away from an unstable CDC pipeline to a full-load migration engineered for speed, validation, and a safe rollback path.

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