Hadoop or Spark?

1. Performance: Spark is faster because it uses random access memory (RAM) instead of reading and writing intermediate data to disks. Hadoop stores data on multiple sources and processes it in batches via MapReduce.
2. Cost: Hadoop runs at a lower cost since it relies on any disk storage type for data processing. Spark runs at a higher cost because it relies on in-memory computations for real-time data processing, which requires it to use high quantities of RAM to spin up nodes.
3. Processing: Though both platforms process data in a distributed environment, Hadoop is ideal for batch processing and linear data processing. Spark is ideal for real-time processing and processing live unstructured data streams.
4. Scalability: When data volume rapidly grows, Hadoop quickly scales to accommodate the demand via Hadoop Distributed File System (HDFS). In turn, Spark relies on the fault tolerant HDFS for large volumes of data.
5. Security: Spark enhances security with authentication via shared secret or event logging, whereas Hadoop uses multiple authentication and access control methods. Though, overall, Hadoop is more secure, Spark can integrate with Hadoop to reach a higher security level.
6. Machine learning (ML): Spark is the superior platform in this category because it includes MLlib, which performs iterative in-memory ML computations. It also includes tools that perform regression, classification, persistence, pipeline construction, evaluation, etc.