To know rack awareness in more detail, refer to the HDFS architecture blog. It is mainly used to perform unit tests. In this way, there is always a running NameNode in the cluster and thus it never fails. It is really very useful and handy, It will serve as anytime reference point :) Enjoyed reading it. if not please share the link it will be helpfull. Hadoop cluster requires to copy/replicate all the data. Facebook adopted the Hive to overcome MapReduce’s limitations. According to the “Replica Placement Policy” two copies of replicas for every block of data will be stored in a single rack whereas the third copy is stored in the different rack. Cheers! Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Pig Latin is a high-level data flow language, whereas MapReduce is a low-level data processing paradigm. It needs high memory space, thus works as a high-end machine with great memory space. We will definitely come up with more Spark-related interview questions. Thanks a lot very much for the high quality and results-oriented help. ♣ Tip: It is recommended to explain the HDFS components too i.e. We have communicated your feedback to the relevant team and will incorporate it soon. Data Architect Interview Questions: Do Your Research Beforehand. How To Install MongoDB On Ubuntu Operating System? Passive “NameNode” is a standby “NameNode”, which has similar data as active “NameNode”. name.dr – identifies the location of metadata storage and specify whether DFS is located on disk or the on the remote location. Then i have prepared for ibps, so now any chances for me to get a big data job if i trained from any institute!! Prior preparation of these top 10 Big Data interview questions will surely help in earning brownie points and set the ball rolling for a fruitful career. Sure and Thanks , But that would be great if you can really find me a recruiter who is willing to hire a fresher provided I come up to his mark. It executes the tasks on given nodes by finding the best task tracker node. We will be happy to answer them. Please take a look: Read frequently asked Apache YARN Interview Questions with detailed answers and examples. Fair Sharing – It defines a pool for each user that contains a number of maps and reduce slots on a resource. FIFO Scheduler – It orders the jobs on the basis of their arrival time in a queue without using heterogeneity. It receives the input from the “mapper” on a particular “node” and sends the output to the “reducer”. As the opportunities for Hadoop are unlimited, the competition for aspirants preparing for the interviews is also high. Pig Latin can handle both atomic data types like int, float, long, double etc. During “aggregation”, we need the output of all the mapper functions which may not be possible to collect in the map phase as mappers may be running on the different machine where the data blocks are stored. Do you mean to ask if our course covers the entire Hadoop framework? It saves a lot of time by performing synchronization, configuration maintenance, grouping and naming. We cannot perform “aggregation” (addition) in mapper because sorting does not occur in the “mapper” function. So, it will consume high network bandwidth and can cause network bottlenecking. Others. Currently, jobs related to Big Data are on the rise. What is the difference between Big Data and Hadoop? The Hadoop project, which Doug Cutting (now Cloudera's Chief Architect) co-founded in 2006, is an effort to create open source implementations of internal systems used by Web-scale companies such as Google, Yahoo!, and Facebook to manage and process massive data volumes. When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Another striking feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. It is important to combine data as Hadoop splits the data into various blocks. What is Hadoop? Because of these two reasons, one of the most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster. Job Tracker manages to monitor the all task trackers individually and then submit the overall job to the client. Big Data Architect Interview Questions # 9) What are the different relational operations in “Pig Latin” you worked with? Read this blog to get a detailed understanding on commissioning and decommissioning nodes in a Hadoop cluster. The NameNode recovery process involves the following steps to make the Hadoop cluster up and running: Whereas, on large Hadoop clusters this NameNode recovery process may consume a lot of time and this becomes even a greater challenge in the case of the routine maintenance. That was the time when big giants like Yahoo, Facebook, Google, etc. The dfs.block.size parameter can be used in the hdfs-site.xml file to set the size of a block in a Hadoop environment. It is responsible to identify the location of data by communicating with NameNode. 31. ♣ Tip: Similarly, as we did in HDFS, we should also explain the two components of YARN: If you want to learn in detail about HDFS & YARN go through Hadoop Tutorial blog. Answer: Checkpointing is a procedure to that compacts a FsImage and Edit log into a new FsImage. Without writing complex Java implementations in MapReduce, programmers can achieve the same implementations very easily using Pig Latin. Apache Oozie is a scheduler which schedules Hadoop jobs and binds them together as one logical work. HDFS is more suitable for large amounts of data sets in a single file as compared to small amount of data spread across multiple files. To understand “Oozie” in detail and learn how to configure an “Oozie” job, do check out this introduction to Apache Oozie blog. Depending on the size of data, the replication of data will take some time. Hive abstracts the complexity of Hadoop MapReduce. 2. Big Data Hadoop Interview Questions and Answers. And lastly, if we try to aggregate data at mapper, it requires communication between all mapper functions which may be running on different machines. In this question, first explain NAS and HDFS, and then compare their features as follows: This is an important question and while answering this question, we have to mainly focus on two points i.e. This is one of the most introductory yet important … It takes in instructions from the ResourceManager and manages resources available on a single node. thanks a lot. Once you have cached a file for your job, Hadoop framework will make it available on each and every data nodes where you map/reduce tasks are running. 12. Whenever some data is stored on HDFS, the NameNode replicates (copies) that data to multiple DataNode. This blog is the gateway to your next Hadoop job. 2 phone screens focused on a mix of high level questions about big data technologies and my experience, a few more specific technical questions about distributed computing concepts, and questions about my preferred working environments, how I work in a team etc. A “SerDe” is a combination of a “Serializer” and a “Deserializer”. -copyFromLocal: This command is used to copy the file from the local file system to the Hadoop System. Keep sharing such kind of worthy information. 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