Spark vs. MapReduce Architecture🔥
Both Apache Spark and Hadoop MapReduce are designed to handle Big Data, but their architectures make them very different.
Spark processes data in-memory, which makes it up to 100x faster than MapReduce that writes data to disk after each step. Spark’s architecture is built around a Driver Program that coordinates Executors, and it uses a Directed Acyclic Graph (DAG) to optimize data flow. It supports both real-time and batch processing, making it versatile and efficient.
On the other hand, MapReduce follows a two-step process (Map → Reduce), where intermediate results are written to disk. This ensures fault tolerance but adds latency. Its architecture revolves around Job Tracker and Task Trackers, which manage jobs in a sequential, disk-heavy manner.
💡 Fun Fact: Spark can run on top of Hadoop’s storage layer (HDFS), combining Spark’s speed with Hadoop’s scalability!
Spark = Speed ⚡ | MapReduce = Stability 💽
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