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Hadoop applications have wide range of technologies that provide great advantage in solving complex business problems. Core components of a Hadoop application are- 1) Hadoop Common. HDFS3) Hadoop Map. Reduce. 4) YARNData Access Components are - Pig and Hive. Data Storage Component is - HBase. Data Integration Components are - Apache Flume, Sqoop, Chukwa. Data Management and Monitoring Components are - Ambari, Oozie and Zookeeper. Data Serialization Components are - Thrift and Avro. Data Intelligence Components are - Apache Mahout and Drill. What is Hadoop streaming? Click here to Tweet. Hadoop distribution has a generic application programming interface for writing Map and Reduce jobs in any desired programming language like Python, Perl, Ruby, etc. This is referred to as Hadoop Streaming. Users can create and run jobs with any kind of shell scripts or executable as the Mapper or Reducers. What is the best hardware configuration to run Hadoop? Click here to Tweet. The best configuration for executing Hadoop jobs is dual core machines or dual processors with 4. GB or 8. GB RAM that use ECC memory. Hadoop highly benefits from using ECC memory though it is not low - end. ECC memory is recommended for running Hadoop because most of the Hadoop users have experienced various checksum errors by using non ECC memory. However, the hardware configuration also depends on the workflow requirements and can change accordingly. What are the most commonly defined input formats in Hadoop? Click here to Tweet. The most common Input Formats defined in Hadoop are: Text Input Format- This is the default input format defined in Hadoop. Key Value Input Format- This input format is used for plain text files wherein the files are broken down into lines. Sequence File Input Format- This input format is used for reading files in sequence. What is Big Data? Click here to Tweet. Big data is defined as the voluminous amount of structured, unstructured or semi- structured data that has huge potential for mining but is so large that it cannot be processed using traditional database systems. Big data is characterized by its high velocity, volume and variety that requires cost effective and innovative methods for information processing to draw meaningful business insights. More than the volume of the data – it is the nature of the data that defines whether it is considered as Big Data or not. Here is an interesting and explanatory visual on “What is Big Data?”We have further categorized Big Data Interview Questions for Freshers and Experienced- Hadoop Interview Questions and Answers for Freshers - Q. Nos- 1,2,4,5,6,7,8,9. Hadoop Interview Questions and Answers for Experienced - Q. Nos- 3,8,9,1. 0For a detailed PDF report on Hadoop Salaries - CLICK HEREHadoop HDFS Interview Questions and Answers 1. What is a block and block scanner in HDFS? Click here to Tweet. Block - The minimum amount of data that can be read or written is generally referred to as a “block” in HDFS. The default size of a block in HDFS is 6. MB. Block Scanner - Block Scanner tracks the list of blocks present on a Data. Node and verifies them to find any kind of checksum errors. Block Scanners use a throttling mechanism to reserve disk bandwidth on the datanode. Explain the difference between Name. Node, Backup Node and Checkpoint Name. Node. Click here to Tweet. Name. Node: Name. Node is at the heart of the HDFS file system which manages the metadata i. Name. Node uses two files for the namespace- fsimage file- It keeps track of the latest checkpoint of the namespace. It is a log of changes that have been made to the namespace since checkpoint. Checkpoint Node- Checkpoint Node keeps track of the latest checkpoint in a directory that has same structure as that of Name. Node’s directory. Checkpoint node creates checkpoints for the namespace at regular intervals by downloading the edits and fsimage file from the Name. Node and merging it locally. The new image is then again updated back to the active Name. Node. Backup. Node: Backup Node also provides check pointing functionality like that of the checkpoint node but it also maintains its up- to- date in- memory copy of the file system namespace that is in sync with the active Name. Node. 3. What is commodity hardware? Click here to Tweet. Commodity Hardware refers to inexpensive systems that do not have high availability or high quality. Commodity Hardware consists of RAM because there are specific services that need to be executed on RAM. Hadoop can be run on any commodity hardware and does not require any super computer s or high end hardware configuration to execute jobs. What is the port number for Name. Node, Task Tracker and Job Tracker? Click here to Tweet. Name. Node 5. 00. Job Tracker 5. 00. Task Tracker 5. 00. Explain about the process of inter cluster data copying. Click here to Tweet. HDFS provides a distributed data copying facility through the Dist. CP from source to destination. If this data copying is within the hadoop cluster then it is referred to as inter cluster data copying. Dist. CP requires both source and destination to have a compatible or same version of hadoop. How can you overwrite the replication factors in HDFS? Click here to Tweet. The replication factor in HDFS can be modified or overwritten in 2 ways- 1)Using the Hadoop FS Shell, replication factor can be changed per file basis using the below command- $hadoop fs –setrep –w 2 /my/test. Explain the difference between NAS and HDFS. Click here to Tweet. NAS runs on a single machine and thus there is no probability of data redundancy whereas HDFS runs on a cluster of different machines thus there is data redundancy because of the replication protocol. NAS stores data on a dedicated hardware whereas in HDFS all the data blocks are distributed across local drives of the machines. In NAS data is stored independent of the computation and hence Hadoop Map. Reduce cannot be used for processing whereas HDFS works with Hadoop Map. Reduce as the computations in HDFS are moved to data. Explain what happens if during the PUT operation, HDFS block is assigned a replication factor 1 instead of the default value 3. Click here to Tweet. Replication factor is a property of HDFS that can be set accordingly for the entire cluster to adjust the number of times the blocks are to be replicated to ensure high data availability. The application splits the file being downloaded in up to ten threads, thus significantly increasing the download speed. Its auto resume option comes handy when the downloading gets interrupted. You can also assign a tag or a few to the downloaded content for accurate and easy search through the downloaded files. 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Enraged, it breaks free from its foundation and chases the group to a construction site. Nebbercracker attempts to distract the house so he can dynamite it, but the house notices and attacks him. Chowder fights it off with an excavator and DJ is given the dynamite. While Chowder distracts the house, DJ and Jenny climb to the top of a crane and DJ throws the dynamite into the chimney causing the house to explode. The trio then see Nebbercracker with Constance's ghost before she fades away. DJ apologizes to Nebbercracker for the loss of his house and wife, but Nebbercracker thanks the kids for freeing him from being trapped for 4. That night, children in their Halloween costumes are lined up at the site of the house, where DJ, Chowder and Jenny help return the toys to their owners. Jenny's mother picks her up and DJ and Chowder go trick- or- treating, which they previously felt they were too old for. Those who were eaten by the house now emerge from the basement. Mitchel Musso as Dustin J. Horace Nebbercracker, a 7. US Army . Walters, DJ's overprotective parents. Production. This process was pioneered by Robert Zemeckis on his film The Polar Express, also produced by Sony Pictures Imageworks. While The Polar Express was produced for the 3- D IMAX 7. Monster House was released in approximately 2. REAL D Cinema digital 3- D stereoscopic projection. The process was not based on film, but was purely digital. Since the original source material was . For the film's release, the studio nicknamed it Imageworks 3. D. Based on 1. 58 reviews collected by review aggregate site Rotten Tomatoes, the film scored a 7. The site's critical consensus reads, . A scary, sharp, funny movie, this is the best kids’ flick of the year so far. It's a 3- D animated kids' film built on classic gothic horror lines, a jokey, spooky Goonies for the new millennium. Monster's pre- teens are sarcastic, think they're smarter than their parents and are going crazy over the opposite sex. Scott of the New York Times commented, . DJ's parents are away, and the house doesn't turn monstrous in front of his teenage babysitter, Zee. But it does turn monstrous in front of her boyfriend, Bones. It doesn't turn monstrous in front of the town's two cops until, in another scene, it does. Rowling's three most important young characters for its lead trio: Tall, dark- haired, serious- minded DJ is Harry, semi- dufus Chowder is Ron and their new cohort, smarty- pants prep school redhead Jenny (Spencer Locke), is Hermione.. Across 9. 0 minutes, however, the experience is desensitizing and dispiriting and far too insistent. British Board of Film Classification. Retrieved October 2. Retrieved November 1. Retrieved August 2. But then this is a kids’ horror.. Retrieved August 2. I can’t imagine a better natural setting for a horror film than adolescence. 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