Technology Stack
Databases Stack
Relational and non-relational databases for scalable data solutions. The frameworks and tools I use to build, experiment, and ship real solutions.
- BuildReal solutions
- ExperimentContinuously
- StayUp to date
- ShareWhat I learn
- Data Modeling
- Indexing
- Transactions
- Caching
- Search
- Vectors
- Migrations
Databases
Relational and non-relational databases for scalable data solutions.
The right store for each job — relational by default, specialised where it pays off.
Overview
Most systems I build start with PostgreSQL or MySQL, then add Redis for caching, Elasticsearch for search and vector stores for AI retrieval.
Good schema design, indexing and safe migrations matter more than the choice of database.
Key Focus Areas
- Relational data modeling
- Query optimisation and indexing
- Caching strategies
- Full-text and semantic search
- Vector storage for RAG
- Migrations and backups
Tools & Technologies
A selection of the tools and platforms I work with.
PostgreSQL
MySQL
MongoDB
Redis
Elasticsearch
SQLite
Supabase
Qdrant- Vector Databases
Kibana
My Experience Level
Not just tools — real hands-on experience.
- PostgreSQL90%
- MySQL90%
- Redis85%
- MongoDB70%
- Elasticsearch70%
- Vector Databases75%
Real-World Applications
How I use these technologies in practice.
- Transactional business systems
- Search and filtering over large catalogs
- Caching hot data and sessions
- Semantic search for AI assistants
- Reporting and analytics queries
Featured Projects
What's Next?
Continuously learning and exploring.
- pgvector at scale
- Hybrid search tuning
- Event sourcing patterns
Learning Resources
Articles and tutorials I've written on these topics.
New articles and tutorials on this topic are on the way.
Let's Build Something Amazing
Have a project in mind or want to collaborate? I'm always open to discussing new opportunities.
Let's Talk