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How to Work With Tuple in Python

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Tuple in python is one of the streaming datasets. The other streaming datasets are List and Dictionary. Operations that you can perform on it are shown here for your reference. Writing tuple is easy. It has values of comma separated, and enclosed with parenthesis '()'. The values in the tuple are immutable, which means you cannot replace with new values. #1. How to create a tuple Code: Tuple example my_tuple=(1,2,3,4,5) print(my_tuple) Output: (1, 2, 3, 4, 5) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #2. How to read tuple values Code: print(my_tuple[0]) Output: 1 ** Process exited - Return Code: 0 ** Press Enter to exit terminal #3. How to add two tuples Code: a=(1,6,7,8) c=(3,4,5,6,7,8) d=print(a+c) Output: (1, 6, 7, 8, 3, 4, 5, 6, 7, 8) ** Process exited - Return Code: 0 ** Press Enter to exit terminal #4.  How to count tuple values Here the count is not counting values; count the repetition of a given value. Code: sample=(1, 6, 7, 8, 3, 4, 5, 6, 7, 8

AWS EMR Vs. Hadoop: 5 Top Differences

With Amazon Elastic MapReduce Amazon EMR, you can analyze and process vast amounts of data. It distributes the computational work across a cluster of virtual servers ( run in the Amazon cloud). An open-source framework of Hadoop manages it. 



AWS EMR Vs. Hadoop




Amazon EMR - Elastic MapReduce, The Unique Features


  • Amazon EMR has made enhancements to Hadoop and other open-source applications to work seamlessly with AWS.
  • For instance, Hadoop clusters running on Amazon EMR use EC2 instances as virtual Linux servers for the master and slave nodes, Amazon S3 for bulk storage of input and output data, and CloudWatch to monitor cluster performance and raise alarms.
  • Also, you can move data into and out of DynamoDB using Amazon EMR and Hive. That orchestrates by Amazon EMR control software that launches and manages the Hadoop cluster. This process is called an Amazon EMR cluster.


What does Hadoop do?


Hadoop uses a distributed processing architecture called MapReduce, in which a task maps to a set of servers for processing.


  • The results of the computation performed by those servers reduce to a single output set.
  • One node, designated as the master node, controls the distribution of tasks. The following diagram shows a Hadoop cluster with the master node directing a group of slave nodes which process the data.
  • One Master node handles multiple slave nodes. All open-source projects run on the Hadoop architecture can also be run on Amazon EMR. The most popular applications, such as Hive, Pig, HBase, DistCp, and Ganglia, are already integrated with Amazon EMR.


By running Hadoop on the Amazon EMR, you will get the following benefits of the cloud:


  1. The ability to provision clusters of virtual servers within minutes.
  2. You can scale the number of virtual servers in your cluster to manage your computation needs and only pay for what you use. 
  3. Integration with other AWS services.

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