Hadoop (MapReduce)
- Uses disk-based batch processing
- Slower due to frequent read/write to disk
- More complex code (typically Java-based)
- Good fault tolerance via HDFS replication
- No native real-time processing (requires external tools like Apache Storm)
- Machine learning via external tools like Apache Mahout
- Best for: large-scale, cost-effective batch jobs
Apache Spark
- Uses in-memory processing (much faster)
- Supports batch, real-time, and streaming workloads
- Easier to use with high-level APIs (Scala, Python, Java, R)
- Efficient fault tolerance using RDD lineage
- Built-in Spark Streaming and Structured Streaming
- Includes MLlib for machine learning
- Best for: fast, iterative tasks, real-time analytics, and machine learning