Why use Spark in a distributed cluster?
💡 Model Answer
Apache Spark is a fast, general-purpose cluster computing system that processes data in-memory across a distributed cluster. It abstracts the cluster into a Resilient Distributed Dataset (RDD) or DataFrame, allowing developers to write parallel operations without managing low-level details. Spark’s DAG scheduler optimizes execution plans, and its lazy evaluation defers computation until an action is called, reducing unnecessary data shuffling. By running on YARN, Mesos, or Kubernetes, Spark can scale horizontally, handling petabytes of data. It supports multiple languages (Python, Scala, Java, R) and integrates with Hadoop HDFS, S3, and other storage systems, making it ideal for ETL, machine learning, and real-time analytics workloads.
This answer was generated by AI for study purposes. Use it as a starting point — personalize it with your own experience.
🎤 Get questions like this answered in real-time
Assisting AI listens to your interview, captures questions live, and gives you instant AI-powered answers — invisible to screen sharing.
Get Assisting AI — Starts at ₹500