Data Technology

Graph Databases for Complex Relationships Graph databases store data as nodes and edges, making it natural to map real-world connections. They shine when data contains many relationships, such as people, places, and events. In contrast, relational databases rely on joins, which can become slow as networks grow. A graph engine traverses links directly, often returning results with simpler queries and predictable latency. How it works A graph model uses nodes for entities and edges for relationships. Each node has properties like name or id, and each edge has a type and its own properties. The schema stays flexible, so you can add new kinds of connections without redesigning tables. This helps teams evolve data models as needs change. ...

Real-Time Data Processing: Streaming Analytics Real-time data processing lets teams turn streams into fast, actionable insights. Streaming analytics focuses on data that flows in continuously, rather than waiting for a batch to finish. This approach helps detect events, anomalies, and trends as they happen. What makes streaming analytics different? It emphasizes low latency, high throughput, and incremental computation. Instead of waiting for a daily end-of-day report, you get near-instant results that can trigger alerts or feed live dashboards. ...