Business is confronted with huge data and to save the data small companies use excel whereas big companies use Oracle and big data. Join the Hadoop Training in Chennai to clear the job interviews. Here are the top Hadoop Interview Questions with Answers.
- Define Relational database, non-relational database, and the HDFC? List out the difference between RDBMS and HDFS?
Definition
Relational databases are also called SQL databases. Some of the popular RDBMS are Microsoft SQL Server, MySQL, Oracle database and IBM DB2. Analytical RDBMS are Analytics platform system, Teradata, and Netezza. Non-relational databases are called as NoSQL databases. Some of the popular non-relational RDBMS are MongoDB, DocumentDB, Cassandra, coach base, HBase, Redis, and Neo4j. HDFS is open source software designed for parallel computing. Any kind of data such as structured, unstructured and semi-structured are stored in HDFS. Hadoop is released in the year 2011 by Apache Software Foundation. Join the Big Data Training in Chennai to sharpen your skills in the basic questions, HDFS questions, MapReduce questions, HBase questions, Sqoop questions, Flume questions, Zookeeper questions, pig questions, hive questions and Hadoop questions.
Difference between RDBMS and HDFS
The difference between the RDBMS and HDFS are the parallel processing, schema on reading and write, the speed of the read and write, cost and type of data type stored in the database. RDBMS is suitable for the online transactional processing {OLTP} whereas HDFS is suitable for the online analytical processing {OLAP}. RDBMS will manage a large set of data with cache and maintain the read consistency whereas the Hadoop use memory cache and doesn’t maintain the read consistency.
- Explain the five V’s of big data?
Big data is the new term in the data management system which is used to process data sets which are difficult to capture, curate, store, search, share, transfer, analyze and visualize in the traditional data processing applications. Big data is used to enhance the business decision making and derive the value of the data with the help of the analytics. The five V’s of big data are volume, velocity, variety, veracity, and value.
Volume– Volume is about the growing size of the data at an exponential rate. Data size is growing from a few dozen terabytes to exabytes of data.
Velocity– Data is changing and named as old data and new data. So, the velocity is about the speed and frequency of the data.
Variety– Big data saves the data in rows and columns. Unstructured data includes text, video, audio, and CSV files. Variety represents the heterogeneity of data.
Veracity: As big data is stored in huge volumes the quality and accuracy is always a matter of question. Veracity: represents the trust, origin, and reputation in the data. One of the examples is Facebook and Twitter where there are data of less quality.
Value
The result of the big data analysis is called as the value. Value is helpful to enable the decisions in the business.
- What are the components of Hadoop?
The main components of Hadoop are HDFS and YARN. HDFS is the storage unit and the YARN is the processing framework. Big Data Training is the best training to get placed with a high salary in the job.
- What are the uses of context object?
The context object communicates with the other Hadoop systems to update the status of any application. Context object consists of all the details of the job and interfaces in the configuration. It helps to generate the output with the help of the configuration information.
- What are the three core methods of a reducer?
The three methods of the core methods are set up, reduce(), and cleanup () to perform the functions such as input, distribute, process the large data size, task to reduce, task to clear temporary files.
- Define shuffle, sort phase and partitioning in Map Reduce?
Shuffle phase takes care of the process of shuffling like performing the map task and reduce task with intermediate outputs. The process of shuffling the map task and the reduce task is called as shuffle phase. Sort phase is the phase which takes of the input before using the reducer. Sorting is sorting the keys generated by the mapper with the help of the Map-reduce framework. The process of receiving value from the reducer is called the partitioning phase. This phase takes place after the map task and before the reduce task.
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- What is custom partitioner in Hadoop Map-reduce? How to write a custom partitioner?
Based on the user needs the custom partitioner store the results in different reducer. Under the partitioner class, a new class is created, the get partition method must be overridden, and the wrapper in the Hadoop Map Reduce adds the configuration file to the custom partitioner which runs the Hadoop Map Reduce. Hadoop Course in Chennai is designed with real-time projects and interview questions to help with the placement.
- Explain the side data distribution techniques in Hadoop?
There are two types of side data distribution techniques in Hadoop. They are using the job configuration and the cache distribution. To get the value in the task the metadata is overridden in the mapper or reducer by configuring method. Configure method and getter method is used to serialize objects. The size of transferring the data is restricted to a few kilobytes as the memory usage is restricted. Distributed cache distributes the datasets. The task nodes copy the files in time and use these files when needed. The files are normally copied to a particular node and the particular job. The first method is a serialization of data whereas the second method is the job configuration of data.
- What are the key components in HBase? How are the key components used in HBase?
The region, Region server, HBase master, Zookeeper, and the catalog tables are the key components in the HBase. The region contains the memory data store in the H file, region server monitors the region, HBase master monitor the region server, Zookeeper takes care of the coordination between the HBase Master and the client, catalog tables take care of ROOT and META ROOT table where the meta table and the regions are stored. HBase is used in big data when the schema is variable; data sets are stored in the form of collections and when the application demands for the key for retrieving the data.
- Explain the difference between HBase and Hive?
Hive is for SQL savvy people to run the map reduce jobs whereas the HBase is for the NoSQL. HBase supports the put, get, scan and the delete operations whereas Hive is used for the analytical purpose.
- What are the commands in the record level and table level in the HBase?
The operational commands in the record level are put, get, increment, scan and delete. The operational commands in the table level the commands are described, list, drop, disable and scan.
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