Tech Foundations

Databases Demystified: From SQL to NoSQL Databases are the quiet engines behind apps. They store facts, support searches, and help keep data accurate. Today you often see two families: SQL databases that use tables and fixed schemas, and NoSQL databases that embrace flexibility through documents, key-values, wide columns, or graphs. Both aim to manage information, but they approach structure, scaling, and queries in different ways. Relational databases use a schema: tables with defined columns, rows, and relationships. SQL lets you join data across tables, filter with predicates, and use indexes to speed up lookups. NoSQL databases come in several flavors: document stores keep JSON-like documents; key-value stores map keys to values; wide-column stores group columns for each row; graph databases focus on connections between items. This diversity helps for different needs. The choice often rests on how predictable your data is and how you plan to search it. ...

Artificial intelligence foundations for developers Building AI features starts with a clear problem and honest constraints. Developers benefit from a simple map: what to know, what to measure, and how to ship safely. This article covers fundamental ideas that help you create reliable AI-powered apps. Core concepts Training vs inference: training tunes a model once; inference runs it to answer many requests. Data quality: good data improves results; biased or noisy data hurts outcomes. Evaluation: pick metrics that reflect user value, not only raw accuracy. Latency and cost: response time and compute price affect the user experience. Transfer learning: reuse existing models to save time and improve results. Data matters Data drives AI behavior. Use clean, representative data and protect user privacy. Minimize data collection, label thoughtfully, and document data sources. If data shifts, you may need to adjust prompts, fine-tune, or update the model version. ...