How is Spark architecture different from Hadoop architecture?

Basically Spark is a young kid who can turn on the T.V. at lightning speed. On the other hand, Hadoop is a granny who takes light-years to do the same. (pun intended). It is a good practice to believe that Spark is never replacing Hadoop. read more

It is a good practice to believe that Spark is never replacing Hadoop. There are many things and cases which allow businesses to choose Hadoop over Spark in production. Differences. 1 . Bad: Hadoop, retrieves data from HDFS, performs a map task - writes to disk - reads this updated block - reduces it - writes to disk. read more

To conclude, Spark Hadoop can take the best parts of Hadoop like YARN (for resource management) and HDFS to make things easy for everyone familiar with Hadoop and combine it with Spark. Spark is not considered as a replacement but as an extension to Hadoop. read more

Basically Spark is a young kid who can turn on the T.V. at lightning speed. On the other hand, Hadoop is a granny who takes light-years to do the same. (pun intended) It is a good practice to believe that Spark is never replacing Hadoop. read more

There are 3 different types of cluster managers a Spark application can leverage for the allocation and deallocation of various physical resources such as memory for client spark jobs, CPU memory, etc. Hadoop YARN, Apache Mesos or the simple standalone spark cluster manager either of them can be launched on-premise or in the cloud for a spark application to run. read more