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Database solutions: SQL vs. NoSQL, optimisation and migration for SMEs
Database decisions – SQL, NoSQL, NewSQL, in-memory – are rarely made in the abstract: they arise in the context of performance problems, migration projects, scaling requirements or legacy replacement. This page clarifies the decision framework and typical scenarios before an architecture decision is locked in.
SQL databases (PostgreSQL, MySQL, SQL Server) are the right choice for structured, transactionally consistent data with complex queries and reporting requirements. NoSQL databases (MongoDB, Redis, Elasticsearch) excel at flexible schemas, high write throughput and specific access patterns (key-value, document, graph, time-series).
Database performance issues most often stem not from technology choice but from missing indexes, suboptimal queries, misconfigured connection pools or growing tables without a partitioning strategy. Analysis before a migration uncovers these root causes.
Technology selection: when to use which database architecture
Relational databases are the first choice when data is structured, transactions require ACID guarantees and reporting is a primary use case. PostgreSQL is the standard recommendation for new mid-market projects: powerful, free and with broad hosting support.
Document databases (MongoDB) suit variable schemas that change rapidly and hierarchical data structures. Graph databases (Neo4j) are right when relationships between entities are the primary query target. Time-series databases (TimescaleDB, InfluxDB) optimise storage and queries for sequential measurement data.
Polyglot persistence – different databases for different data types in the same system – increases technical debt and operational complexity. It makes sense when requirement differences are large enough; otherwise a well-configured relational database is more maintainable.
Database optimisation and migration strategy
Before a migration decision, performance analysis comes first: slow query log, execution plans (EXPLAIN ANALYZE), index usage statistics and storage growth trends. In the majority of cases, performance problems can be resolved through optimisation measures on the existing database rather than migration.
Database migrations on production systems require precise zero-downtime strategies: schema changes with dual-write phases, blue-green deployments or event-sourcing approaches for history transfer. Maintenance-window migrations are not an option for high-availability systems.
Data migration is more demanding than schema migration: data transformation, deduplication, consistency checks and rollback scenarios are realistic time positions in project planning.
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