Database Trends: What is changing in the database world (besides AI)
Earlier this month, I had a half-hour chat with Kellyn Gorman, a Database and AI Advocate and Engineer at Redgate. The UK software company is known for database DevOps and database management tools most databases – and since 2024 as the owner of DB-Engines popularity Ranking of database management systems.
The chat was an intellectual pleasure, to say the least. Kellyn is outstandingly well informed on databases, with a background starting in Oracle, spanning most databases as a DBA and industry analyst, and by now using MariaDB for about fifteen years, almost since its inception.
We published the interview as a Youtube trilogy
- Part 1. Database Trends — What is changing in the database world
- Part 2. Choosing between MySQL, MariaDB and PostgreSQL
- Part 3. The Future of Databases and AI
In this blog, I will look into the conclusions: What we can learn from the conversation — and how should we interpret it?
Understanding database ranking sites
1. Popularity metrics are useful — but only one lens
DB-Engines and similar rankings provide a shared reference point across the industry. At the same time, they capture signals rather than the full reality of how databases are used.
“DB-Engines is one of the main rankings people refer to… but it’s complex.”
2. Perception and real-world usage do not always align — especially for MariaDB vs MySQL
How databases are discussed and counted does not always reflect how they are used in practice. This is particularly evident for MariaDB and MySQL: they share origins and compatibility, yet are distinct systems with different trajectories — something not always fully understood in the market.
“When you have MariaDB, it is MariaDB. It isn’t MySQL.”
“Each unique database has its reasons for existing in the ecosystem.”
Database trends: What is changing in the database world?
3. Real-world decisions are driven by operational context
Database choices are rarely about features alone. They are shaped by uptime requirements, existing systems, and the ability to operate reliably in production.
“It doesn’t matter if you’re doing an upgrade or a migration — getting the data where it needs to be, in the new environment, and not have downtime is crucial when we’re talking about a mission-critical system.”
4. Migration complexity is a defining constraint
Migration is often the most significant practical factor in decision-making — and it is frequently underestimated.
“Migration projects… can go on for three, four years, and they’re still not complete.”
“Many organizations are told… that it’s easy to migrate… and the migration comes to a stall.”
AI and data systems
5. AI increases the importance of databases
Rather than replacing databases, AI depends on them. Structured, reliable data remains where the core value resides.
“This is where our most valuable data is — in our relational systems.”
6. Data systems are becoming more tightly coupled with applications
The boundary between data infrastructure and application logic is becoming less distinct, increasing the demands on how data systems are designed and operated.
PostgreSQL and adoption dynamics
7. Adoption is shaped by both technical and non-technical factors
Database choices are influenced by capabilities, but also by perception, familiarity, and organisational context.
“Users have a different choice than just ‘Postgres everywhere’.”
The MySQL ecosystem
8. The MySQL–MariaDB ecosystem is both unified and fragmented
There is an inherent tension: MariaDB and MySQL share a common heritage and a high degree of compatibility — yet they are distinct databases with diverging paths.
They are similar enough to be interchangeable in many contexts, yet different enough to require conscious choice.
“When you have MariaDB, it is MariaDB. It isn’t MySQL.”
9. Vendor strategy influences ecosystem direction
Changes in investment, governance, and priorities shape how database ecosystems evolve over time.
“You don’t need to throw Oracle at everything.”
Migration: what decisions users are facing
10. Migration is difficult, slow, and often underestimated
In practice, migration projects are long-running, complex, and uncertain.
“I see migration projects going from Oracle to Postgres. They can go on for three, four years, and they’re still not complete.”
11. Compatibility can dramatically reduce migration effort
Where compatibility exists, the difference can be profound — both in time and cost.
“Most processes are not going to require any kind of refactoring or rewrite.”
“That’s going to decrease your migration time.”
“We’re talking 100-fold.”
Conclusion: why this matters
12. Cost, control, and flexibility remain key decision factors
Ultimately, organisations choose database platforms based on long-term control, cost efficiency, and their ability to evolve without disruption.
“It has saved millions and millions of dollars in licensing fees.”
Final words
What emerges from the conversation is a pattern: database decisions are increasingly shaped by operational reality — migration complexity, ecosystem fit, and long-term control — rather than isolated technical features. In that landscape, compatibility, clarity, and the ability to evolve without disruption become decisive.
