Data-Infrastructure

Streaming data architectures for real time analytics Streaming data architectures enable real-time analytics by moving data as it changes. The goal is to capture events quickly, process them reliably, and present insights with minimal delay. A well-designed stack can handle high volume, diverse sources, and evolving schemas. Key components Ingestion and connectors: Data arrives from web apps, mobile devices, sensors, and logs. A message bus such as Kafka or a managed streaming service acts as the backbone, buffering bursts and smoothing spikes. ...

Big Data Fundamentals for Data-Driven Businesses Big data refers to very large, fast-moving data from many sources. For a business, it means more signals to guide decisions, not just last quarter results. The aim is to turn raw data into reliable insights that everyone in the company can use. Three ideas help guide practice: volume, velocity, and variety—the classic three Vs, with veracity and value added. Volume is the sheer amount of data from sensors, apps, and logs. Velocity is how quickly new data comes in. Variety covers many formats, from text to video. Veracity reminds us to check trust, and value keeps the goal in sight. ...