Hadoop Real-Time Processing: Methods & Techniques

The following methods and techniques can be used to implement real-time data processing in a Hadoop environment:

1. Utilizing Apache Kafka as a message queue, stream real-time data into the Kafka cluster.
2. Utilize Apache Storm or Apache Flink as the real-time data processing engine to handle data streams in Kafka.
3. Utilize Apache HBase or Apache Cassandra as real-time data storage to persist processed data.
4. Integrating Apache Spark Streaming and Hadoop MapReduce for real-time and batch data processing, achieving a fusion of real-time and offline processing.
5. Leverage Apache NiFi for efficient data flow management and monitoring to ensure high reliability and availability for real-time data processing.
6. Utilize Hadoop YARN for resource management to ensure efficient execution of real-time data processing tasks.

By combining the above methods and technologies, efficient real-time data processing can be achieved in the Hadoop environment to meet business requirements for real-time processing.