Which one is better: Hadoop or Spark?

The choice between Hadoop and Spark depends on your specific requirements, use cases, and the nature of your data processing needs. Both Hadoop and Spark are big data processing frameworks, but they have different characteristics and use cases.

1. Hadoop:

   • MapReduce Paradigm: Hadoop's original strength lies in the MapReduce programming model, which is effective for batch processing of large datasets.
   • Storage: Hadoop Distributed File System (HDFS) is the storage system used by Hadoop. It is designed to store and manage large volumes of data across multiple nodes.
   • Batch Processing: Hadoop is well-suited for batch processing, where you have a large amount of data to process in a non-real-time fashion.

2. Spark:

   • In-Memory Processing: Spark is designed for in-memory data processing, making it faster than Hadoop for certain use cases. It keeps intermediate data in memory, reducing the need to write to disk.
   • Multiple Processing Engines: Spark provides various libraries for different purposes, such as Spark SQL for SQL queries, Spark Streaming for real-time data processing, MLlib for machine learning, and GraphX for graph processing.
   • Ease of Use: Spark offers a more user-friendly API and supports multiple programming languages, including Scala, Java, Python, and R.

Considerations:

• If your primary concern is batch processing of large-scale data and fault tolerance, Hadoop might be a suitable choice.
• If you need real-time or near-real-time data processing, iterative algorithms, and a more user-friendly API, Spark could be a better fit.

In many cases, organizations are moving towards Spark due to its performance advantages and the flexibility it provides for various data processing tasks. However, the choice ultimately depends on your specific use case, the nature of your data, and your processing requirements.