Over the past decade, businesses have shifted from batch processing to streaming analytics to meet rising customer expectations for real-time insights, driven by the demand for instant interactions and faster decision-making. This transition is facilitated by enterprise-ready solutions that make streaming more accessible, reducing the need for extensive in-house engineering expertise. The rise of large language models (LLMs) emphasizes the importance of fresh, real-time data for maintaining competitive advantage in AI and machine learning applications. Additionally, regulatory requirements in industries like banking and healthcare necessitate real-time monitoring to ensure compliance and prevent violations. As a result, streaming analytics has become essential for businesses across various sectors, helping them prevent fraud, optimize operations, and improve customer experiences, marking a significant evolution in data strategy from the traditional batch processing approach.