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10 Big Data Interview Questions You Need to be Prepared for

Data is being generated at an unprecedented rate today, and it has become increasingly difficult to store, process, and analyze this data using traditional methods. Businesses need to make informed and logical decisions by analyzing infinite data streams. This is where big data comes in. It refers to a comprehensive approach that combines Artificial Intelligence (AI) with traditional analysis tools. This blog looks at the most pressing big data interview questions and overviews the aspects of big data, such as its definition, challenges, and the tools and technologies used to process and analyze it.

What is Big Data and How do You Define it?


Among the most vital big data interview questions, big data refers to extremely large and complex data sets. These data sets are inaccessible to traditional data processing tools and technologies. It is characterized by the 4 V’s: volume, velocity, variety, and veracity. Data is generated from a variety of sources, such as social media, sensors, and databases. This necessitates the use of sophisticated tools and technologies to store, process, and analyze it.


What are the Different Types of Big Data?


Big data is classified into structured, semi-structured, and unstructured. Structured data is information that has been organized into a specific format, such as a database. Semi-structured data is information in a format that is only partially defined, such as XML or JSON. Unstructured data, which includes text, images, and videos, has no predefined format.


What is Hadoop and How Does it Relate to Big Data?


Another important topic covered in big data interview questions is Hadoop. Hadoop is a free open-source software framework for storing, processing, and analyzing large and complex data sets. Hence, it is intended to handle big data by utilizing a distributed computing model that allows multiple computers to collaborate to process and analyze data.


Define HDFS and YARN and Discuss Their Individual Components


HDFS, or the Hadoop Distributed File System, is Hadoop’s primary storage system for large and complex data sets. Moreover, it is intended to distribute data storage across multiple machines. This ensures high availability and fault tolerance. The components of HDFS include:

  • NameNode: It is the “master” node of the HDFS cluster that manages the file system namespace and regulates client access to files

  • DataNode: The HDFS cluster’s “slave” node, it stores data in the form of blocks on its local disk

  • Secondary NameNode: This isn’t a backup for the NameNode but it helps with file housekeeping

YARN, or Yet Another Resource Negotiator, is a Hadoop resource management framework that handles resource management in a distributed computing environment. It manages the Hadoop cluster’s resources (CPU, memory, and network) and is in charge of allocating resources to the various applications running on the cluster. YARN’s components include:

  • The Resource Manager: It is the “master” node of the YARN cluster and is in charge of managing the cluster’s resources

  • Node Manager: It is the “slave” node and is in charge of managing the resources of the individual nodes of the cluster

  • Application Master: It manages the execution of a specific application on the cluster by negotiating resources with the Resource Manager while also coordinating with the Node Managers

What is MapReduce and How Does it Work in Hadoop?

MapReduce is used in Hadoop to divide large data sets into smaller, more manageable chunks that can be processed concurrently across multiple computers.


What Exactly is Apache Spark, and How is it Different From Hadoop?


Apache Spark is a free and open-source data processing engine built to handle large and complex data sets. It is distinct from Hadoop in the way it employs an in-memory data processing model, allowing it to process data much faster than Hadoop.


Name the Three Modes in Which Hadoop Can be Run

  1. The Standalone Mode: Regarded as the default Hadoop mode, the standalone mode’s primary function is debugging. It uses the local file system for both input and output operations. However, it does not support HDFS. Primarily, as it lacks the custom configuration needed for the mapred-site.xml, core-site.xml, and hdfs-site.xml files.

  2. The Pseudo-Distributed Mode: Also known as the single-node cluster, it includes both the NameNode and the DataNode on the same machine. All Hadoop daemons run on a single node in this mode, implying the “master” and “slave” nodes are the same.

  3. Fully Distributed Mode: Also known as a multi-node cluster, this mode allows multiple nodes to run Hadoop jobs at the same time. All of the Hadoop daemons run on different nodes in this case. As a result, the “master” and “slave” nodes operate independently.


How do You Handle Missing or Incomplete Data in Big Data?


In big data, dealing with missing or incomplete data requires a variety of techniques, such as data imputation, deletion, or estimation. Imputation of data entails filling in missing values with statistical techniques such as mean, median, mode, or regression. Deletion entails removing the entire row or column containing missing values, but this can result in data loss too. Estimation is predicting missing values based on other variables in the data set using advanced machine learning algorithms. Hence, the technique used is determined by the type of data, the extent of missing values, and the analysis requirements.


What Exactly is a Distributed Cache? What are its Advantages?


A Distributed Cache is a Hadoop feature that allows files, archives, and other resources to be cached across multiple nodes in a Hadoop cluster. The cached data can be shared between tasks, allowing for efficient data processing in distributed environments. Here are some advantages of using a Distributed Cache:


Improved system performance: Caching frequently used data can reduce the time required for data processing, significantly improving system performance.


Reduced network traffic: Storing data in the cache reduces the need to connect to the network. This reduces network traffic and speeds up data processing.


Better resource utilization: It improves cluster resource utilization as the Distributed Cache enables efficient resource sharing across multiple tasks.


Flexibility: The Distributed Cache allows for the dynamic addition and removal of data from the cache, aiding greater data processing flexibility.



What are Some of the Difficulties Associated with Working with Big Data? How Can You Overcome Them?


Data quality issues, data privacy, and security concerns, and the need for specialized tools and technologies are some of the challenges of working with big data. These difficulties can be overcome by implementing data quality checks, encryption, and access control to protect data, and investing in training and development to gain the necessary skills and expertise.

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