Comparing Real-Time Data Processing with Batch Processing

 For our Marketing Analytics project, my team and I took a deep dive into Real-Time Analytics—how it differs from traditional Batch Processing and why both approaches matter for data-driven decision-making. We explored real-time analytics’ ability to handle incoming data on the fly, giving businesses immediate insights that can be used to personalize user experiences, detect fraud, or optimize inventory in near-real time. In contrast, batch processing collects data over a set period and analyzes it in larger chunks, offering powerful insights but with higher latency.

Our presentation walks through the benefits and challenges of each method, focusing on marketing applications. Real-time analytics can boost customer satisfaction through timely recommendations and proactive issue resolution, but it requires specialized infrastructure and continuous monitoring. Meanwhile, batch processing is often easier to manage for complex, large-scale data, though it lacks the instant responsiveness that today’s fast-paced markets sometimes demand. Below are the slides from our final presentation, showcasing real-world use cases, the pros and cons of each approach, and why striking the right balance between real-time and batch processing can be a game-changer in marketing analytics.