SQL vs NoSQL Databases: How to Choose
SQL vs NoSQL explained by data shape and access pattern, with concrete examples for relational, document, key-value, wide-column and graph databases.
The actual amount of data or work a system successfully processes in a given period of time.
Throughput measures real output, while bandwidth measures theoretical capacity. A network link might have a bandwidth of 1 Gbps, but congestion, protocol overhead, packet loss, and distance may limit actual throughput to much less.
The concept applies well beyond networking. A database's throughput might be measured in transactions per second, a web server's in requests per second, and an AI inference service's in tokens generated per second. Throughput and latency are related but distinct: a system can handle a high volume of requests while each individual request still takes a noticeable time. Engineers improve throughput through parallel processing, batching, caching, load balancing, and removing bottlenecks identified through monitoring and observability.
The maximum amount of data that can be transmitted over a network connection in a given time, usually measured in bits per second.
LatencyThe time delay between a request being sent and a response beginning to arrive, usually measured in milliseconds.
Load BalancerA system that distributes incoming network traffic across multiple servers so no single server is overwhelmed.
SQL vs NoSQL explained by data shape and access pattern, with concrete examples for relational, document, key-value, wide-column and graph databases.
What load testing is, how load, stress, spike and soak tests differ, and how to read results like percentiles and error rates before real traffic finds your limits.
Where AI actually pays off in logistics and supply chain, from demand planning to routing and warehouses, and the data you need before each one works.