Hadoop primarily relies on the MapReduce programming model, which is a batch processing system. This means that it's well-suited for large-scale data processing tasks that don't require real-time processing. On the other hand, Spark is designed for in-memory data processing and can handle both batch and real-time processing, making it more versatile.
In terms of performance, Spark generally outperforms Hadoop in most scenarios due to its in-memory processing capabilities. This allows Spark to process data faster than Hadoop, especially when iterative algorithms are involved.
As for ease of use, Spark has a more developer-friendly API and supports multiple programming languages like Python, Scala, and Java, making it more accessible for developers. Hadoop's API, while powerful, is more complex and typically requires more effort to work with.
In a big data project, I would choose Hadoop if the primary focus is on cost-effective, large-scale batch processing, and fault tolerance is a top priority. Hadoop's distributed file system, HDFS, is highly reliable and fault-tolerant. On the other hand, I would choose Spark if the project requires real-time processing or iterative machine learning algorithms, and if the team has experience with languages like Python or Scala.