Describe the challenges and solutions for handling real-time data processing in a distributed environment. How do technologies like Apache Kafka and Spark Streaming address these challenges?

Simulated

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.

96.7%

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

0.8%

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

2.5%

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

0.0%

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Describe the challenges and solutions for handling real-time data processing in a distributed environment. How do technologies like Apache Kafka and Spark Streaming address these challenges?