How do Hadoop and Spark differ in data processing?
An example of an AI chatbot conversation about comparing Hadoop and Spark for data processing.
What are the main differences between Hadoop and Spark?
Hadoop is primarily a storage and batch processing framework, while Spark is designed for in-memory processing and real-time analytics.
Which one is faster for data processing?
Spark is generally faster than Hadoop because it processes data in memory, reducing the time spent on disk I/O.
Can you explain the use cases for each?
Hadoop is great for large-scale batch processing and data storage, while Spark excels in real-time data processing and machine learning tasks.
Is it possible to use them together?
Yes, many organizations use Spark on top of Hadoop's HDFS for storage, leveraging the strengths of both technologies.
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