In the digital era, real-time data processing has become crucial for delivering interactive user experiences and facilitating decision-making in distributed applications like microservices. This necessity has led to the widespread adoption of stream processing technologies such as Apache Spark, Apache Flink, and ksqlDB, each offering unique capabilities for handling large-scale data streams. Apache Spark, known for its fast in-memory processing, supports multiple languages and integrates well with technologies like Redpanda, though it demands high memory consumption and has a steep learning curve. Apache Flink, designed natively for stream processing, is praised for its low latency and easy-to-use features but lacks the community support that Spark enjoys. Meanwhile, ksqlDB leverages Apache Kafka's infrastructure to provide a simple SQL interface for stream processing, excelling in Kafka integration but falling short in analytics capabilities compared to Spark and Flink. All three tools can be used with Redpanda to perform tasks such as real-time analysis and fraud detection, allowing companies to choose based on specific needs and use cases.