In addition to its fault-tolerant and scalable architecture, Spark offers built-in support for complex event processing, making it ideal for real-time streaming applications that require handling of a large volume of event data. However, it's important to note that Spark's streaming capabilities are micro-batch based, meaning it introduces slight latency in processing. Moreover, managing stateful operations in real-time streaming can be complex in Spark, and thus, alternative technologies like Apache Flink might be more suitable for certain use cases.

The advantages of using Spark for real-time streaming applications are its fault-tolerant and scalable architecture, which allows for high-throughput and low-latency processing. Additionally, Spark's built-in machine learning libraries enable real-time analytics and AI capabilities. However, Spark's reliance on in-memory processing can be a limitation for applications with large data volumes, requiring careful memory management. Similarly, while Spark provides low-latency processing, it may not be suitable for ultra-low latency use cases.

Spark shines in real-time streaming applications due to its ability to handle large-scale data processing with fault tolerance. This makes it suitable for use cases such as real-time fraud detection, log monitoring, and ETL processes. However, Spark's inherent reliance on memory can cause performance issues if not carefully managed. Additionally, Spark's streaming API operates on discrete batch intervals, which may not align with ultra-low latency requirements. In such cases, other stream processing frameworks like Apache Kafka Streams or Apache Flink might be more appropriate.