In today's data-driven world, businesses utilize streaming and real-time analytics to derive timely insights from data as it is generated, though these approaches differ in their operation and use cases. Streaming analytics involves continuously analyzing data as it flows into the system, providing immediate insights from sources like IoT devices and social media, and is characterized by extremely low latency. Real-time analytics, while also focused on quick data processing, typically involves querying stored data with minimal delay, suitable for applications like personalized advertising or operational dashboards. Differences in data ingestion, latency, and architecture distinguish the two, with streaming analytics requiring systems designed for continuous data flows and real-time analytics relying on fast querying and low-latency data retrieval. Use cases for streaming analytics include fraud detection and social media monitoring, while real-time analytics is often applied to customer personalization and dynamic pricing. Both approaches face challenges: streaming analytics must manage high-velocity data and latency, while real-time analytics must balance query performance with data freshness. Despite these challenges, each approach serves distinct purposes, with streaming analytics excelling in scenarios requiring instant decision-making and real-time analytics providing timely insights without the need for immediate data streams.