Which processing engine is known for handling both batch and stream processing in Big Data?

The processing engine that is notably recognized for its capability to handle both batch and stream processing in Big Data is Apache Beam. This framework provides a unified model that allows users to define data processing pipelines in a language-agnostic way, meaning you can write your processing logic once and run it on multiple execution engines, including Apache Spark and Apache Flink, which are often utilized for stream and batch processing.

Apache Beam's versatility in managing both types of data processing makes it particularly powerful in scenarios where data may be continuously generated (stream) while still needing to aggregate or analyze historical data (batch). This feature is crucial for businesses that require real-time analytics without sacrificing the insights gained from historical data sets.

The other options do have specific strengths: for example, Apache Spark is primarily known for its batch processing but has added capabilities to handle stream processing. Apache Kafka is a distributed event streaming platform that is optimized for handling real-time data feeds but does not directly manage batch processing itself. Apache Hadoop is mainly focused on batch processing using the MapReduce paradigm and does not have native support for stream processing. Consequently, the distinct advantage of Apache Beam lies in its comprehensive ability to seamlessly integrate both batch and streaming processing within its framework.