Hadoop vs Spark

Hadoop and Spark are both open-source big data processing frameworks that are used for storing and processing large amounts of data. However, there are some key differences between the two:

1. Hadoop is a batch processing system, while Spark is a real-time processing system. This means that Hadoop is better suited for processing large amounts of data in batch mode, while Spark is better for processing data in real-time.

2. Hadoop is based on the MapReduce programming model, which is a batch-oriented programming model that is optimized for processing large amounts of data. Spark, on the other hand, is based on the Resilient Distributed Datasets (RDD) programming model, which is a distributed memory-based programming model that is better suited for real-time processing.

3. Hadoop is a more established and mature technology, while Spark is relatively newer and still evolving.

4. Hadoop is generally considered to be more powerful and flexible than Spark, but Spark is generally faster and easier to use.

In summary, Hadoop and Spark are both powerful tools for big data processing, but they are suited for different use cases. Hadoop is better for batch processing, while Spark is better for real-time processing.