yes you are right..Apache Spark was designed at AMPLab of UC Berkley as an alternative to Hadoop’s MapReduce. Spark was later given to the Apache software foundation. But, unlike Hadoop, it is easy, fast, and supports real-time streaming. Apache organizations claim Spark as a lightning-fast cluster computing technology. Even though Spark was built as a processing engine for Hadoop data, it is not dependent on Hadoop due to its very own cluster management.
Apache Spark has in-memory computing where data is kept in RAM instead of slow disk drives. This enables users to store and process huge amounts of data at a low cost. This increases the performance of spark by 100x as compared to hadoop mapreduce.
Spark comes with multiple libraries for ML and graph algorithms. It is not only a versatile platform but also supports programming languages like Java, Scala, Python, and R.
All these vast features of Apache Spark make it the first choice for companies like Apple, Facebook, and Microsoft.
yes you are right..Apache Spark was designed at AMPLab of UC Berkley as an alternative to Hadoop’s MapReduce. Spark was later given to the Apache software foundation. But, unlike Hadoop, it is easy, fast, and supports real-time streaming. Apache organizations claim Spark as a lightning-fast cluster computing technology. Even though Spark was built as a processing engine for Hadoop data, it is not dependent on Hadoop due to its very own cluster management.
Apache Spark has in-memory computing where data is kept in RAM instead of slow disk drives. This enables users to store and process huge amounts of data at a low cost. This increases the performance of spark by 100x as compared to hadoop mapreduce.
Spark comes with multiple libraries for ML and graph algorithms. It is not only a versatile platform but also supports programming languages like Java, Scala, Python, and R.
All these vast features of Apache Spark make it the first choice for companies like Apple, Facebook, and Microsoft.