Db

A database relies on several underlying components working together to safely process, store, and fetch queries.

Driven by the explosion of generative artificial intelligence and Large Language Models (LLMs), vector databases store data as high-dimensional mathematical representations called embeddings. They allow software to perform semantic similarity searches rather than exact keyword matching. A database relies on several underlying components working

(e.g., markdown table, plain text, JSON, HTML, ready to copy into Word/Excel) Codd at IBM proposed the

Data is stored as documents, key-value pairs, graphs, or wide columns. Schema: Dynamic and flexible schema. Language: Varies by database type (e.g., JSON querying). In the 1960s

Understanding how databases work, their varied architectures, and how to select the right tool for a specific workload is critical for any modern software engineer, data architect, or tech professional. Core Database Architectures: SQL vs. NoSQL

The concept of a DB is not new. In the 1960s, the first navigational databases—like the Integrated Data Store (IDS)—appeared. These early systems forced developers to navigate through records in a fixed hierarchy or network. Then, in the 1970s, Edgar F. Codd at IBM proposed the , which revolutionized how data was stored and queried. Relational DBs (RDBMS) like Oracle, MySQL, and PostgreSQL soon dominated the landscape.

Historically, companies separated transactions (OLTP) from analytics (OLAP). Modern cloud databases now process live transactional records and heavy analytical scripts concurrently within a single unified database layer.