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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: 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) print(sample

Top 100 Hadoop Complex interview questions (Part 4 of 4)

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Hadoop framework is most popular in data analytics and data related projects. I have given here my 4th set of questions for you to read quickly. 1) What is MapReduce? Ans) It is a framework or a programming model that is used for processing large data sets over clusters of computers using distributed programming. 2). What are ‘maps’ and ‘reduces’? Ans). ‘Maps‘ and ‘Reduces‘ are two phases of solving a query in HDFS. ‘Map’ is responsible to read data from input location, and based on the input type, it will generate a key-value pair, that is, an intermediate output in the local machine. ’Reducer’ is responsible to process the intermediate output received from the mapper and generate the final output. 3). What are the four basic parameters of a mapper? Ans) The four basic parameters of a mapper are LongWritable, text, text, and IntWritable. The first two represent input parameters and the second two represent intermediate output parameters. 4). What are the four basic parame

Top 100 Hadoop Complex Interview Questions (Part 3 of 4)

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These are complex Hadoop interview questions. This is my 3rd set of questions useful for your interviews (3 of 4).      1). What are the features of Standalone (local) mode? Ans). In stand-alone mode there are no daemons, everything runs on a single JVM. It has no DFS and utilizes the local file system. Stand-alone mode is suitable only for running MapReduce programs during development. It is one of the least used environments. 2). What are the features of Pseudo mode? Ans). The pseudo mode is used both for development and in the QA environment. In the Pseudo mode, all the daemons run on the same machine. 3). Can we call VMs as pseudos? Ans). No, VMs are not pseudos because VM is something different and pseudo is very specific to Hadoop. 4). What are the features of Fully Distributed mode? Ans). The fully Distributed mode is used in the production environment, where we have ‘n’ number of machines forming a Hadoop cluster. Hadoop daemons run on a cluster of machines. There i

Top 100 Hadoop Complex Interview Questions (Part 2 of 4)

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I am giving a series of Hadoop interview questions. This is my 2nd set of questions. You can get quick benefits by reading these questions from start to end. 1). If a data Node is full how it’s identified? Ans). When data is stored in a data node, then the metadata of that data will be stored in the Namenode. So Namenode will identify if the data node is full. 2). If data nodes increase, then do we need to upgrade Namenode? Ans). While installing the Hadoop system, Namenode is determined based on the size of the clusters. Most of the time, we do not need to upgrade the Namenode because it does not store the actual data, but just the metadata, so such a requirement rarely arise. 3). Are job tracker and task trackers present in separate machines? Ans). Yes, job tracker and task tracker are present in different machines. The reason is job tracker is a single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. 4). When we send a da

Top 100 Hadoop Complex Interview Questions (Part 1 of 4)

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The below list is complex interview questions as part of Hadoop tutorial (part 1 of 4) you can go through these questions quickly. 1. What is BIG DATA? Ans). Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques. 2. Can you give some examples of Big Data? Ans). There are many real-life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of sensor data for every 30 minutes of flying time. All these are a day to day examples of Big Data! 3. Can you give a detailed overview of the Big Data being generated by Facebook?   Ans). As of December 31, 2012, there are 1.06 billion monthly active users on Facebook and 680 million mobile users. On an average,

Big data: Quiz-1 Hadoop Top Interview Questions

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In this post, I have given a Quiz on Big data with answers. This is part-1 set of questions for your quick reference. Photo credit: Srini Q.1) How Hadoop achieve scaling in terms of storage? A.By increasing the hard disk capacity of the machine B.By increasing the RAM capacity of the machine C.By increasing both the hard disk and RAM capacity of the machine D.By increasing the hard disk capacity of the machine and by adding more machine Q.2) How fault tolerance with respect to data is achieved in Hadoop? A.By breaking the data into smaller blocks and distributing these smaller blocks into several machines B.By adding extra nodes. C.By breaking the data into smaller blocks and copying each block several times, and distributing these replicas across several machines. By doing this Hadoop makes sure even if the machines are failed the replica is present in some other machine D.None of these Q.3) In what all parameters Hadoop scales up? A. Storage only B. Performan