FAQ
Short answers to the questions that usually come up before a team commits to a shortlist.
Choose based on your workload, not trends. Consider data structure, read/write patterns, consistency requirements, and scaling needs. Use a guided database selector to narrow down options based on real-world constraints instead of guesswork.
Use SQL databases for structured data, strong consistency, and complex queries. Choose NoSQL when you need flexibility, horizontal scaling, or handle unstructured or rapidly changing data models.
Yes, PostgreSQL is a strong default for many applications. It supports relational data, JSON, indexing, and extensions, making it suitable for both transactional and moderately analytical workloads.
Use a document database when your data is semi-structured, evolves frequently, or needs flexible schemas. It works well for content systems, user profiles, and rapidly changing product requirements.
OLTP workloads benefit from relational databases with strong consistency and fast transactions. Choose systems optimized for high write throughput, indexing, and low-latency operations.
Analytical workloads require columnar storage, fast aggregations, and large-scale scans. Use data warehouses or OLAP systems designed for querying large datasets efficiently.
Use a vector database when working with embeddings, semantic search, or AI-powered recommendations. It enables similarity search over high-dimensional data like text, images, and audio.
Some modern databases support multiple workloads, but most systems specialize. For production systems, it is often better to use a combination of databases optimized for each use case.
AI applications often combine traditional databases with vector stores. Use a primary database for transactions and a vector database for semantic retrieval and recommendation systems.
Real-time applications require low latency, high throughput, and efficient scaling. Choose databases optimized for fast reads and writes, in-memory performance, or event-driven architectures.
Compare databases based on workload fit, consistency model, scalability, operational complexity, and ecosystem support. Avoid choosing based on popularity alone.
For MVPs, choose a database that is easy to set up, well-documented, and flexible. PostgreSQL or a managed NoSQL database are common choices depending on your use case.
For MVPs, choose a database that is easy to set up, scalable, and well-supported. PostgreSQL is a strong default, while NoSQL databases work well for flexible or rapidly evolving schemas.
Most web applications use relational databases like PostgreSQL or MySQL due to their reliability, strong consistency, and support for complex queries and transactions.
Large-scale systems require databases that support horizontal scaling, partitioning, and replication. Distributed SQL or NoSQL databases are commonly used depending on consistency and workload needs.
Real-time applications need low latency and high throughput. In-memory databases or highly optimized NoSQL systems are often used for real-time feeds, messaging, and live updates.
Analytics workloads benefit from columnar databases or data warehouses designed for fast aggregation, large-scale queries, and business intelligence use cases.
AI systems often combine traditional databases with vector databases. Use a primary database for structured data and a vector database for semantic search and embedding-based retrieval.
Mobile apps typically use lightweight local databases like SQLite along with backend databases such as PostgreSQL or Firebase for syncing and real-time updates.
E-commerce systems require strong consistency for orders and payments, along with scalability for traffic spikes. Relational databases are commonly used, often combined with caching layers.
In microservices, each service can use a database suited to its needs. This approach, called polyglot persistence, allows better scalability and flexibility across services.
Applications with heavy read traffic benefit from databases with strong indexing, caching, and replication strategies to serve data quickly and efficiently.
Write-heavy systems require databases optimized for fast ingestion, append operations, and horizontal scaling. NoSQL and log-based systems are often a good fit.
Time-series workloads require efficient handling of timestamped data, compression, and fast aggregations. Specialized time-series databases are optimized for these use cases.
Logging systems require high ingestion rates and fast querying. Search engines and log-optimized databases are commonly used for observability and monitoring.
IoT systems generate large volumes of time-series data and require scalable ingestion, efficient storage, and real-time processing capabilities.
Social platforms require high scalability, low latency, and flexible schemas. A combination of relational, NoSQL, and caching systems is often used.
SaaS systems need multi-tenancy, scalability, and reliability. Relational databases are commonly used with careful schema design and tenant isolation strategies.