Big Data Hadoop Interview Questions and Answers

The term that refers to the grouping and collection of data sets that are complex and large which makes it highly complex to operate with the help of traditional data processing applications and database management tools is known as the big data. Actually, big data which has been a trend in the emerging multinational companies is highly complex to capture, store, curate, and share, analyze, transfer, search and visualize. With big data applications, companies can extract desired values from their data and can stand ahead of their competitors by taking optimized business decisions and building several productive opportunities to improve their business process.

The 5Vs of big data are as follows: Volume, Velocity, Variety, Veracity and Value.

Apache Hadoop which is a framework with a collection of several services and tools that are necessary to process and store big data at ease actually emerged as a solution for the problems that were associated with the big data. The process like analyzing big data and instantly bringing out decisions that are in line with the analysis to enhance business operation was simpler and efficient with Hadoop rather than the old traditional processing methods.

The two main components of Hadoop are as follows:

Storage Unit – It is nothing but HDFS which is a collection of NameNode and DataNode

Processing Framework – It is particularly YARN which includes ResourceManager and NodeManager

HDFS is referred to as Hadoop Distributed File System which is actually a Hadoop storage unit that undergoes master and slave topology. HDFS stores a collection of various data in the form of blocks in a specific distributed environment.

HDFS includes two components namely NameNode and DataNode.

NameNode: A master node of the distributed environment which effectively maintains all the essential and required metadata information like block location and replication factors of the data blocks that are stored in the Hadoop is known as the NameNode.

DataNode: A reliable slave node that is required to store data effectively in HDFS is known as the DataNode which is managed by the NameNode known as master node.

Once a data is reliably stored in HDFS, the function of the NameNode is to replicate the specific data to various DataNode present, the replication factor is almost 3 in default condition and this factor can be configured on necessity. In case a DataNode is performing slower than usual, then the NameNode will copy the replicas of the data automatically to another better performing node and keep the data always available and this process is known as the HDFS false tolerant function.

A tiny continuous location present in the hard drive where essential data are stored reliably and securely is known as blocks, these blocks are stored in HDFS and are evenly catered across various Hadoop clusters.

It uses the throttling mechanism to track the list of blocks that are available in the DataNode and checks for any errors.

The physical data division is called HDFS block while a logical data division is known as an input split. HDFS actually divided the available data in the form of blocks and stores all the blocks together while to process MapReduce splits every data into an input split and distributed it to the particular mapper function.