Latest Database Posts

Relational Database Management Systems (RDBMS)remain foundational infrastructure in modern distributed systems because they provide strong consistency, transactional guarantees, and mature tooling for data integrity. Large-scale systems such as payment platforms, logistics networks, and SaaS product

NoSQL engines provide different trade-offs inlatency, consistency, scalability, and query flexibility. Choosing the right engine requires understandingdata access patterns, workload characteristics, and operational complexity, not just database type. Choosing the right NoSQL engine directly impactsl

Many production data bugs originate not from missing transactions, but from misunderstanding what transactions actually guarantee. Systems often wrap logic inBEGINandCOMMITand assume correctness follows automatically. Under concurrent load, these systems still oversell inventory, double-charge users

Database transactions are the foundation of correctness in almost every non-trivial system. Any system handling money transfers, inventory updates, or user state changes relies on transactions to prevent partial writes and inconsistent reads. A payment service that debits an account but fails to cre

Online Analytical Processing (OLAP) engines power analytical workloads involving aggregations, scans, and complex queries over large datasets. Selection impacts query latency, cost efficiency, scalability limits, and operational complexity. Different engines optimize for columnar storage, distribute

NoSQL databases are designed to handlelarge-scale, high-velocity, and unstructured datathat traditional relational databases struggle with. They often sacrifice strict consistency forscalability, flexibility, and performance, making them ideal for modern distributed systems. Choosing the right NoSQL

PostgreSQLis an open-source relational database management system (RDBMS) known for standards compliance, extensibility, and strong transactional guarantees. It implementsACIDsemantics by default and supports advanced features such as JSONB, window functions, partial indexes, and custom data types.A

After implementing horizontal scaling on servers and handling thousands of concurrent requests inScalability for Dummies — Part 1: Clones, performance may still degrade over time. Applications often slow down and eventually fail due to database limitations. Even with a perfectly scaled front end, th

OLAP databases(Online Analytical Processing) are optimized for complex queries, aggregations, and large-scale analytics workloads. Unlike transactional systems, they focus on read-heavy operations, enabling fast insights across massive datasets, making them essential for BI tools, reporting pipeline

This case study describes a production blog platform backed entirely byAmazon DynamoDB. The system serves typical blog workloads such as “latest posts”, “most popular posts”, “posts by author”, and “posts by tag”, while also supporting administrative queries like user moderation and contact messages