SQL Server to PostgreSQL Migration:
Everything You Need to Know

Moving from Microsoft SQL Server to PostgreSQL is one of the most strategic decisions a modern enterprise can make. With SQL Server licensing costs continuing to rise and cloud-native PostgreSQL delivering enterprise-grade performance at a fraction of the price, thousands of organizations worldwide are making the switch.

But migration is not a simple “lift and shift.” SQL Server and PostgreSQL differ in syntax, data types, stored procedure language, and optimization techniques. This guide walks you through everything — from planning to production cutover — so you can migrate with confidence.

Why Migrate from SQL Server to PostgreSQL?

The business case for migrating to PostgreSQL is compelling. Here are the top drivers:

Key Differences to Understand Before You Start

SQL Server and PostgreSQL share ANSI SQL foundations but diverge significantly in syntax, data types, and procedural extensions. Understanding these differences up front prevents costly surprises during migration.

The Migration Process: Step by Step

A successful SQL Server to PostgreSQL migration follows a structured six-phase approach. Skipping phases — especially assessment and testing — is the primary cause of migration failures.

• 1

  Discovery & Assessment

  Inventory all databases, schemas, stored procedures, views, triggers, and application SQL queries. Identify complexity hotspots — especially T-SQL-specific syntax, cursors, and linked server dependencies.

• 2

  Schema Conversion

  Convert DDL from SQL Server syntax to PostgreSQL. Map data types (NVARCHAR → VARCHAR/TEXT, DATETIME → TIMESTAMP, BIT → BOOLEAN), convert identity columns, and update constraint syntax.

• 3

  Data Migration

  Export data from SQL Server and load into the new PostgreSQL schema. For large databases, use bulk COPY operations for performance. Validate row counts and checksums after load.

• 4

  Code & Procedure Conversion

  Rewrite T-SQL stored procedures, functions, and triggers in PL/pgSQL. This is typically the most time-consuming phase — automated tools like DMAP can convert 80–90% automatically.

• 5

  Application Layer Testing

  Run your application against the new PostgreSQL database. Test all CRUD operations, stored procedure calls, ORM queries, and connection pooling behavior. Fix any incompatibilities surfaced during testing.

• 6

  Performance Tuning & Cutover

  Rebuild indexes using PostgreSQL best practices (B-tree, GIN, BRIN). Analyze query plans with EXPLAIN ANALYZE, update statistics, and configure autovacuum. Execute the cutover during a planned maintenance window with rollback capability.

A Quick Syntax Example: T-SQL to PL/pgSQL

Here is a simple example of how a SQL Server stored procedure translates to a PostgreSQL function:

-- SQL Server T-SQL
CREATE PROCEDURE GetActiveUsers
  @DeptID INT
AS BEGIN
  SELECT TOP 100 UserID, FullName, Email
  FROM Users
  WHERE DepartmentID = @DeptID
    AND IsActive = 1
    AND CreatedAt >= DATEADD(day, -30, GETDATE());
END

-- PostgreSQL PL/pgSQL equivalent
CREATE OR REPLACE FUNCTION get_active_users(
  p_dept_id INTEGER
) RETURNS TABLE(user_id INT, full_name TEXT, email TEXT)
LANGUAGE plpgsql AS $$
BEGIN
  RETURN QUERY
  SELECT userid, fullname, email
  FROM users
  WHERE department_id = p_dept_id
    AND is_active = TRUE
    AND created_at >= NOW() - INTERVAL '30 days'
  LIMIT 100;
END;
$$;

Common Challenges and How to Overcome Them

1. Case Sensitivity

SQL Server is case-insensitive by default for identifiers and comparisons. PostgreSQL folds unquoted identifiers to lowercase and is case-sensitive for quoted ones. Audit your application code and SQL for any mixed-case dependencies before migrating.

2. NULL Handling Differences

Both databases follow ANSI SQL NULL semantics, but subtle differences in how NULL flows through certain functions and comparisons can cause unexpected results. Thorough regression testing is essential.

3. Implicit Data Type Casting

SQL Server performs many implicit casts automatically (e.g., INT to VARCHAR in concatenations). PostgreSQL requires explicit casting with :: notation or CAST(). Your migration tool or team needs to handle this systematically.

4. Large T-SQL Codebase

Organizations with hundreds of stored procedures, views, and triggers face the heaviest conversion burden. Manual rewriting is slow and error-prone — this is where automated conversion platforms deliver the highest ROI.

Tools for a Smooth Migration

Several tools can assist at different phases of a SQL Server to PostgreSQL migration:

How Newt Global DMAP Accelerates SQL Server Migration

Newt Global’s DMAP (Database Migration Acceleration Platform) was purpose-built to solve the hardest parts of enterprise database migration — at scale, with automation, and with enterprise-grade security.

Pre-Migration Checklist

Before starting your SQL Server to PostgreSQL migration, confirm you have covered the following:

Conclusion: Make the Move with Confidence

Migrating from SQL Server to PostgreSQL is a transformative step toward a more open, flexible, and cost-effective data infrastructure. The technical challenges are real — T-SQL differences, data type mismatches, and the sheer scale of enterprise codebases — but they are entirely solvable with the right strategy and tooling.

Whether you are a startup looking to cut licensing costs or a large enterprise modernizing your data stack for the cloud, PostgreSQL delivers the performance, extensibility, and community support to meet your needs for years to come.

Newt Global’s DMAP platform removes the biggest barriers to migration by automating the most labor-intensive conversion tasks, giving your team a clear, validated path from SQL Server to a cloud-native PostgreSQL architecture — in weeks, not months.