Real-time data processing in a distributed environment faces challenges like data consistency, latency, and fault tolerance. Apache Kafka and Spark Streaming address these by providing scalable and fault-tolerant data streaming capabilities.

Real-time data processing is straightforward in a distributed environment and does not require special handling or technologies.

Real-time data processing challenges are best addressed by batch processing technologies, which are more reliable and efficient.

Real-time data processing should be avoided due to its inherent complexity and the difficulty of achieving low latency and high throughput.