The market for ‘BigData’ is experiencing a tremendous growth, thereby creating a huge demand for skilled and trained BigData professionals across the globe. Though the demand is massive, the supply certainly seems to be falling short of the demand. The core reason might be the lack of proper homework before attending the interviews.
To make things smoother for you during the interview preparation process, we have listed top 50 commonly asked questions along with the best suited answers, which can help you to successfully crack the BigDataHadoop interview.
Note: All the questions and answers are prepared by the subject experts who are associated with Kovid Academy.
1. What is Big-Data?
The term ‘Big-data’ is used to represent a collection of large and complex datasets, which are difficult to capture, store, process, share, analyze, and visualize using the traditional RDBMS tools.
2. Explain the five V’s of Big Data?
Big-Data is often described using the five V’s, which are:
Volume – the amounts of data generated every day, i.e. in Petabytes and Exabytes.
Velocity – the speed at which the data is generated every second. After the advent of social media, it probably takes seconds for any news to get viral across the Internet.
Value – having access to bigdata is always a good thing, but failing to extract the real value from it is completely useless. Extracting value means, drawing benefits to the organizations; achieving the return on investment (ROI); and making profits for the businesses working on big data.
3. On what concept the Hadoop framework works?
The Hadoop Framework works on:
Hadoop Distributed File System: HDFS is a Java-based storage unit in Hadoop, which offers reliable and scalable storage of large datasets. It is responsible for storing different types ofdata in the form of blocks.
Hadoop MapReduce: MapReduce is a Java-based programming paradigm that offers scalability across different Hadoop clusters. It is responsible for distributing the workload into different tasks to run in parallel. The job of ‘Map’ is to split the datasets into tuples or key-value pairs, and the ‘Reduce’ then takes the output from Map and combines it with datatuples into a smaller set of tuples.
Hadoop YARN: Yet Another Resource Negotiator is the architectural framework in Hadoop that allows multiple data processing engines to handle storeddata in a single platform, disclosing a new completely method to analytics.
Note: Reduce jobs are performed only after the execution of Map jobs.