Hadoop is Real Time

Hadoop, in its traditional form, is not considered a real-time processing framework. Hadoop’s primary strength lies in batch processing of large volumes of data. However, there are ways to incorporate real-time or near-real-time processing into a Hadoop-based data processing pipeline:

1. Hadoop Ecosystem Components for Real-Time:

   •  Spark: While Hadoop’s MapReduce is designed for batch processing, Apache Spark, which is often used alongside Hadoop, provides real-time data processing capabilities. Spark’s in-memory processing and built-in libraries for streaming data (Spark Streaming) allow you to perform real-time analytics on data as it arrives.

   • HBase:  HBase is a NoSQL database that can be integrated with Hadoop. It is known for its real-time read and write capabilities, making it suitable for applications requiring low-latency data access.

2. Lambda Architecture: Some organizations implement a Lambda Architecture, which combines batch processing (Hadoop) with real-time processing (e.g., Apache Spark Streaming or Kafka). This approach allows you to handle both historical and real-time data efficiently.

3. Data Ingestion and Streaming: To achieve real-time or near-real-time processing with Hadoop, you need to ensure that data ingestion into your Hadoop cluster is as close to real-time as possible. Apache NiFi, Flume, or Kafka can be used for ingesting streaming data into Hadoop.

4. Machine Learning Models: You can integrate machine learning models developed using tools like Spark MLlib or TensorFlow with your Hadoop-based data processing pipeline to perform real-time predictions and recommendations.

5. Interactive Querying: While not real-time in the strictest sense, tools like Apache Hive, Impala, or Presto can provide near-real-time interactive querying of data stored in Hadoop, allowing for quick exploration and analysis.

6. Hadoop on Cloud Services: Cloud-based Hadoop services like Amazon EMR or Azure HDInsight provide options for real-time data processing by integrating with services like AWS Kinesis or Azure Stream Analytics.