Hadoop and storm these are the frameworks are used to analyze the big data.

so to be frank both of these are complement to each other but they are different few aspects Apache storm does all the operations except persistency means it won’t store any data in other hand Hadoop is good at everything but it lacks when it comes to real-time computation so this table will describe exactly the differences between storm and Hadoop so if you see here storm is good for a real-time processing and Hadoop is good for batch processing means sending or processing a group of messages at a time and storm is stateless that is the reason storm is using Apache zookeeper and Hadoop is stateful and here storm will use master/slave architecture and the master node is Carles Nimbus and slaves are called as supervisor Hadoop also will follow the same architecture that is master slave and the master is called as job tracker and slave is Carla’s task Kraken and a storm streaming process can access tens of thousands of messages per seconds in a cluster Hadoop uses HDFS means Hadoop distributed file system and ETA it in turn uses MapReduce framework to process vast amount of that data that takes minutes or hours storm topology runs until shutdown by the user are in unexpected failure in case of Hadoop MapReduce jobs are executed in a sequential order and completed eventually and in case of storm if Nimbus our supervisor goes down the restarting storm makes it to continue from where it is top and nothing is get affected but in case of Hadoop if the job tracker dies all the running jobs are lost but the common things between Apache storm and Hadoop ease both are distributed and fault tolerant file tolerant is nothing but it will use master and slave architecture then master goes down slave will add test master so that is nothing but fault or and mode


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