Spark over Hadoop

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                 Spark over Hadoop

 

Spark over Hadoop is a common approach to processing large-scale data efficiently. Apache Spark is a fast and general-purpose cluster computing system for big data processing. It can run on top of the Hadoop Distributed File System (HDFS) and other distributed storage systems. You can achieve faster data processing than traditional Hadoop MapReduce jobs by utilizing Spark’s in-memory processing capabilities.

Spark provides a higher-level API and supports multiple languages, including Scala, Java, Python, and R. It includes libraries for SQL queries, machine learning, graph processing, and streaming data. Spark can be great for tasks like ETL (Extract, Transform, Load), data analysis, and machine learning.

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Hadoop Training Demo Day 1 Video:

 
You can find more information about Hadoop Training in this Hadoop Docs Link

 

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