Big Data

Big Data

Datasets that are so large or complex that traditional data processing applications cannot handle them effectively.

The three Vs of Big Data

Volume, Velocity, and Variety; these define the characteristics and challenges of big data.

Volume

Refers to the vast amounts of information in big data, often exceeding terabytes or petabytes.

Velocity

Describes the speed at which data is generated and needs to be processed, often in near real-time.

Variety

Indicates the various forms of data in big data, including structured, semi-structured, and unstructured formats.

Functional Programming

A programming paradigm that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data.

Fact-based model

A representation method where each data point is an immutable fact, timestamped to prevent overwriting previous data.

Graph Schema

A visual representation of data relationships through nodes and edges, where nodes represent entities and edges represent relationships.

Data Mining

A technique used to derive actionable insights from big data by identifying patterns and relationships.

Machine Learning

An advanced technique for analyzing big data that enables systems to learn from data and make predictions.