How to Convert SQL Server to PostgreSQL

‍Key Takeaways

1. Converting SQL Server to PostgreSQL moves data and schema to an open-source platform.
2. Migration tools like pgloader, DBConvert, and AWS SCT simplify the process.
3. Data integrity checks and backups are crucial before conversion.
4. Stored procedures and triggers may require manual rewriting.
5. Proper planning reduces downtime and ensures a smooth transition.

Why U.S. Companies Are Migrating from SQL Server to PostgreSQL

The database migration trend is accelerating across American enterprises, driven by compelling financial and technical factors. While SQL Server has been a trusted workhorse for decades, the evolving technology landscape makes PostgreSQL an increasingly attractive alternative for forward-thinking organizations.

The Compelling Cost Advantage

The most immediate driver for migration is financial. SQL Server Enterprise Edition costs approximately $14,000 per core, with Standard Edition around $3,700 per core. When you add approximately 20% annually for Software Assurance, the total cost of ownership becomes substantial. For a typical organization with three production servers (16 cores each), first-year costs can exceed $800,000.

PostgreSQL eliminates these licensing fees entirely. As an open-source solution released under the PostgreSQL License, it's free for any purpose, including commercial use Even accounting for managed services like AWS RDS or Azure Database for PostgreSQL, organizations typically achieve 60-70% reduction in database licensing costs. One of our clients, a U.S. e-commerce platform, reduced their annual database costs from $89,000 to $28,000 while improving performance by 28%.

Performance and Flexibility Benefits

Beyond cost savings, PostgreSQL offers technical advantages that make it superior for modern applications:

• Advanced indexing with multiple index types (B-tree, Hash, GiST, SP-GiST, GIN, BRIN) provides flexibility that SQL Server's indexing cannot match.
• Native JSON/JSONB support enables better performance with semi-structured data than SQL Server's limited JSON capabilities
• Superior full-text search capabilities built directly into the core database
• Extensibility through extensions like PostGIS for geospatial data, pgvector for AI workloads, and TimescaleDB for time-series data

For U.S. companies building cloud-native applications, PostgreSQL's cross-platform support and compatibility with all major cloud providers provide crucial flexibility compared to SQL Server's more limited deployment options.

Key Differences Between SQL Server and PostgreSQL

Understanding the fundamental differences between these systems is crucial for planning a successful migration. From our experience with hundreds of migrations, thorough preparation in this area prevents costly issues during implementation.

Architectural Distinctions

At their core, these databases differ significantly in their approach to data management. SQL Server is a pure Relational Database Management System (RDBMS), while PostgreSQL describes itself as an Object-Relational Database Management System (ORDBMS). This means PostgreSQL extends the relational model with support for object-oriented concepts like inheritance, providing greater flexibility for complex data types.

The concurrency models also differ substantially. PostgreSQL uses Multi-Version Concurrency Control (MVCC) to allow efficient concurrent access without read locks, while SQL Server relies on traditional locking mechanisms. This architectural difference often results in better performance for read-heavy workloads in PostgreSQL.

Syntax and Language Variations

While both databases use SQL, their dialects differ significantly. SQL Server uses Transact-SQL (T-SQL) with proprietary extensions, while PostgreSQL uses standard SQL with its procedural language PL/pgSQL.

Here are some of the most common syntax differences we encounter in U.S. enterprise migrations:

Data Type Mapping Challenges

One of the most complex aspects of migration involves converting data types between systems. While some mappings are straightforward, others require careful planning:

• NVARCHAR to VARCHAR: PostgreSQL uses UTF-8 encoding by default, making NVARCHAR unnecessary
• DATETIME to TIMESTAMP: PostgreSQL's TIMESTAMP type handles both DATETIME and DATETIME2
• UNIQUEIDENTIFIER to UUID: PostgreSQL supports UUID natively with the uuid-ossp extension
• IMAGE/VARBINARY(MAX) to BYTEA: For large binary files, consider cloud object storage instead

Case sensitivity presents another significant challenge. SQL Server is case-insensitive by default, while PostgreSQL is case-sensitive. We often implement the CITEXT extension to maintain case-insensitive behavior where needed.

Pre-Migration Assessment and Planning

A successful migration begins with thorough assessment and strategic planning. Rushing this phase inevitably leads to costly delays and issues during execution. Our methodology at HakunaMatataTech involves a structured approach that has proven effective across hundreds of enterprise migrations.

Comprehensive Database Inventory

Start by cataloging everything in your SQL Server environment.

This includes:

• Counting all tables, views, stored procedures, functions, and triggers
• Identifying application connections, ETL jobs, reports, and API dependencies
• Documenting performance baselines including query performance and peak load metrics
• Analyzing SQL Server-specific features that may not have direct PostgreSQL equivalents

This inventory provides the foundation for estimating effort, identifying potential challenges, and planning the migration sequence. For one healthcare client, this process revealed undocumented dependencies that would have caused significant production issues if missed.

Convert SQL Server to PostgreSQL

The actual conversion process represents the technical core of your migration project. Through our work with 500+ enterprise migrations, we've refined this process into a reliable, repeatable methodology that ensures data integrity while minimizing downtime.

Schema Conversion Strategies

Schema conversion involves translating your SQL Server database structure into PostgreSQL-compatible DDL. For U.S. companies with complex databases, we recommend a multi-layered approach:

Automated Tool Conversion: Begin with automated schema conversion tools, which handle the majority of straightforward conversions:

• AWS Schema Conversion Tool (AWS SCT) provides comprehensive schema conversion with assessment reporting
• Ispirer SQLWays offers commercial-grade conversion with support for complex T-SQL at higher price points ($5K-$50K)
• EDB Migration Toolkit specializes in moving other databases into PostgreSQL, with strong Oracle and SQL Server support

These tools typically achieve 70-80% conversion accuracy for standard schema objects. However, they struggle with complex T-SQL constructs, proprietary SQL Server features, and some data type mappings.

Manual Review and Refinement: The automated output requires careful manual review, focusing on:

• Data type mappings that need adjustment
• Index conversion, particularly clustered indexes (which PostgreSQL doesn't have)
• Constraint and default value validation
• Identity column conversions (IDENTITY to SERIAL or GENERATED AS IDENTITY)

For one e-commerce client, manual refinement revealed that 23% of their indexes needed optimization for PostgreSQL's different performance characteristics.

Stored Procedure and Function Migration

Converting T-SQL to PL/pgSQL represents one of the most labor-intensive aspects of migration. The languages differ significantly in syntax, variable declaration, and error handling.

How HakunaMatataTech Simplifies It

The HakunaMatataTech DB Migration Tool automates most of this complexity with:

• Automated Schema Conversion: Intelligently maps SQL Server schema and datatypes to PostgreSQL formats.
• Incremental Sync: Keeps databases synchronized until final cutover with <10 minutes downtime.
• Integrity Validation: Transaction-level checksums ensure zero data loss.
• 50% Faster Migration: Parallel streaming pipelines cut migration time in half for large datasets.

Best For: Enterprises moving from Microsoft ecosystems to open-source or cloud-native PostgreSQL for scalability and cost efficiency.

Data Migration Execution

With schemas converted, the actual data transfer begins. For U.S. enterprises with large databases, we recommend these proven approaches:

pgLoader for Small to Medium Databases: The pgLoader tool provides excellent performance for databases up to 500GB. Its automatic type conversion and parallel processing capabilities make it ideal for straightforward migrations:

LOAD DATABASE
  FROM mssql://user:pass@sqlserver:1433/ProductionDB
  INTO postgresql://user:pass@postgres:5432/ProductionDB

WITH include drop, create tables, create indexes, reset sequences,
     workers = 8, concurrency = 1

SET maintenance_work_mem TO '512MB',
    work_mem TO '128MB'

CAST type datetime to timestamptz drop default drop not null using zero-dates-to-null,
     type uniqueidentifier to uuid using byte-vector-to-bytea;

AWS DMS for Enterprise Migrations: For larger databases or those requiring minimal downtime, AWS Database Migration Service offers robust change data capture (CDC) capabilities. It supports both one-time migrations and ongoing replication, making it ideal for phased migrations. One financial client used AWS DMS to migrate their 2TB database with just 15 minutes of actual downtime.

Custom ETL Processes: For highly complex migrations with significant transformation requirements, custom ETL processes using tools like Pentaho Data Integration or Talend provide maximum control. These are particularly valuable when the migration includes substantial data cleansing or restructuring.

Post-Migration Validation and Optimization

Completing the data transfer is only the beginning. Rigorous validation and performance tuning ensure your migration delivers the expected benefits without introducing new issues.

Data Integrity Verification

Thorough validation is non-negotiable for enterprise migrations. Our process includes:

• Row count comparisons between source and target for all tables
• Checksum validation to ensure data accuracy
• Sample data comparison across different data types and table sizes
• Referential integrity testing to confirm constraints work correctly
• Application functionality testing with the new database

For each of our 500+ migrations, we maintain a comprehensive validation checklist with over 150 specific validations. This rigorous approach is why we've maintained our 100% data integrity record.

Performance Tuning in PostgreSQL

PostgreSQL's performance characteristics differ significantly from SQL Server. Post-migration tuning typically delivers 20-40% performance improvements:

Configuration Optimization: Adjust key PostgreSQL parameters based on your workload:

• shared_buffers (typically 25% of RAM)
• work_mem for sorting and hash operations
• maintenance_work_mem for vacuuming and indexing
• effective_cache_size based on available memory

Index Optimization: Implement PostgreSQL-specific indexing strategies:

• BRIN indexes for large, naturally ordered tables
• GIN indexes for JSONB columns and full-text search
• Partial indexes for queries that target specific data subsets
• Expression indexes for queries that use functions on columns

One manufacturing client achieved a 60% improvement in reporting query performance simply by implementing appropriate PostgreSQL indexes that didn't exist in their SQL Server environment.

Conclusion

If you're considering a database migration, begin with a comprehensive assessment. Understand your current environment, identify potential challenges, and develop a realistic timeline. For most enterprises, a phased approach managed by experienced migration specialists delivers the optimal balance of risk and reward.

At HakunaMatataTech, we've helped hundreds of U.S. companies navigate this journey successfully. Our proven methodology ensures seamless migration with guaranteed data integrity. Contact us today for a free migration assessment and discover how PostgreSQL can transform your database infrastructure while significantly reducing costs.