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Big Data & Hadoop Latest Interview Questions with Answers by Garuda Trainings


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Big Data & Hadoop Latest Interview Questions with Answers by Garuda Trainings.
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Big Data & Hadoop Latest Interview Questions with Answers by Garuda Trainings

  1. 1. BIG DATA/ HADoop InTervIew QuesTIons
  2. 2. 1.What is Big Data? Big data is data that exceeds the processing capacity of traditional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.
  3. 3. 2.What is NoSQL? NoSQL is a whole new way of thinking about a database. NoSQL is not a relational database. The reality is that a relational database model may not be the best solution for all situations. The easiest way to think of NoSQL, is that of a database which does not adhering to the traditional relational database management system (RDMS) structure. Sometimes you will also see it revered to as 'not only SQL'.
  4. 4. 3.We have already SQL then Why NoSQL? NoSQL is high performance with high availability, and offers rich query language and easy scalability. NoSQL is gaining momentum, and is supported by Hadoop, MongoDB and others. The NoSQL Database site is a good reference for someone looking for more information.
  5. 5. 4.What is Hadoop and where did Hadoop come from? By Mike Olson: The underlying technology was invented by Google back in their earlier days so they could usefully index all the rich textural and structural information they were collecting, and then present meaningful and actionable results to users. There was nothing on the market that would let them do that, so they built their own platform. Google’s innovations were incorporated into Nutch, an open source project, and Hadoop was later spun-off from that. Yahoo has played a key role developing Hadoop for enterprise applications.
  6. 6. 5.What problems can Hadoop solve? By Mike Olson: The Hadoop platform was designed to solve problems where you have a lot of data — perhaps a mixture of complex and structured data — and it doesn’t fit nicely into tables. It’s for situations where you want to run analytics that are deep and computationally extensive, like clustering and targeting. That’s exactly what Google was doing when it was indexing the web and examining user behavior to improve performance algorithms.
  7. 7. 6.What is the Difference between Hadoop and Apache Hadoop? There is no diff, Hadoop, formally called Apache Hadoop, is an Apache Software Foundation project.
  8. 8. 7.Why would NoSQL be better than using a SQL Database? And how much better is it? It would be better when your site needs to scale so massively that the best RDBMS running on the best hardware you can afford and optimized as much as possible simply can't keep up with the load. How much better it is depends on the specific use case (lots of update activity combined with lots of joins is very hard on "traditional" RDBMSs) - could well be a factor of 1000 in extreme cases.
  9. 9. 8.Name the modes in which Hadoop can run? Hadoop can be run in one of three modes: i. Standalone (or local) mode ii. Pseudo-distributed mode iii. Fully distributed mode
  10. 10. 9.What do you understand by Standalone (or local) mode? There are no daemons running and everything runs in a single JVM. Standalone mode is suitable for running MapReduce programs during development, since it is easy to test and debug them.
  11. 11. 10.What is the idea behind HDFS? Where does HDFS fail? HDFS is built around the idea that the most efficient approach to storing data for processing is to optimize it for write once, and read many approach. Cannot support large number of small files as the file system metadata increases with every new file, and hence it is not able to scale to billions of files. This file system metadata is loaded into memory and since memory is limited, so is the number of files supported.
  12. 12. 11.What are the ways of backing up the filesystem metadata? There are 2 ways of backing up the filesystem metadata which maps different filenames with their data stored as different blocks on various data nodes: Writing the filesystem metadata persistently onto a local disk as well as on a remote NFS mount. Running a secondary namenode.
  13. 13. 12.What are the functions of JobTracker in Hadoop? Once you submit your code to your cluster, the JobTracker determines the execution plan by determining which files to process, assigns nodes to different tasks, and monitors all tasks as they are running. If a task fail, the JobTracker will automatically relaunch the task, possibly on a different node, up to a predefined limit of retries. There is only one JobTracker daemon per Hadoop cluster. It is typically run on a server as a master node of the cluster.
  14. 14. 13.What is MapReduce in Hadoop? Hadoop MapReduce (Hadoop Map/Reduce) is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks.
  15. 15. 14.What are the benefits of block transfer? A file can be larger than any single disk in the network. There is nothing that requires the blocks from a file to be stored on the same disk, so they can take advantage of any of the disks in the cluster. Making the unit of abstraction a block rather than a file simplifies the storage subsystem. Blocks provide fault tolerance and availability. To insure against corrupted blocks and disk and machine failure, each block is replicated to a small number of physically separate machines (typically three). If a block becomes unavailable, a copy can be read from another location in a way that is transparent to the client.
  16. 16. 15.What is the meaning of speculative execution in Hadoop? Why is it important? Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others. This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.
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