Spark vs Hadoop: A Comparison
When comparing Apache Spark and Apache Hadoop, it's essential to understand their distinct roles in big data processing. Here's a breakdown:
- Speed ⚡: Spark's in-memory processing makes it faster for iterative tasks, while Hadoop relies on disk-based computation.
- Architecture 🏗️: Spark operates on top of Hadoop, using its distributed storage but with its own processing engine.
- Use Cases 🌐: Hadoop excels in batch processing of large datasets, whereas Spark is ideal for real-time analytics and machine learning.
For a deeper understanding of the big data ecosystem, check out our Big Data Overview.