This concise Book is intended to be a ready reference and a checklist for migrating your databases, applications and services from Microsoft SQL Server to PostgreSQL and discusses all the important information you need to be aware of before beginning a migration project.
I have had the opportunity of designing and executing a large migration involving several big SQL Server Instances (with over 100 databases in total) to AWS Aurora (PostgreSQL compatible) with a total size of over 20 terabytes. During the migration, many mistakes were made, and a lot of rework done resulting in several learnings for the team. These learnings are shared here.
While I have taken every effort to make sure that the information presented here is accurate; things might have changed, features that were not supported in older versions might be supported in newer versions, etc. If you find something inaccurate, misleading or grossly wrong, please feel free to leave a comment with the details and the links to support your claims, I would be glad to update the Book with the correct information.
This Book is divided into four chapters:
- Introduction to Open Source Software, its advantages and disadvantages
- Moving from Licensed software to Open Source software
- Basics and the history of Microsoft SQL Server and PostgreSQL
- Choosing the right PostgreSQL Edition and Version for your needs
- Production, Development & Testing Workloads
- On-Premise vs Cloud comparison
- In-depth comparison of SQL Server and PostgreSQL
- Concurrency Control
- Database Structure comparison
- Feature comparison
- Data Type Mapping
- Built-In Functions and Operators
- SQL Syntax differences
- Areas where PostgreSQL has an edge
- Areas where SQL Server has an edge
- GUI Client Tools
- Schema migration
- Data migration
- Code migration
- Jobs migration
- ETL/ELT Tools
- Reporting Platforms
- Analytics Platforms
- Scheduling Options
- Application/Services Migration
- Maintenance and Monitoring
- High Availability, Load Balancing, and Replication
- Environment Stabilization via Parallel Deployment
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