Hadoop Big Data Developer interview Q&A by Top Experts
The market for ‘Big Data’ is experiencing a tremendous growth, thereby creating a huge demand for skilled and trained Big Data 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 Big Data Hadoop 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 data sets, 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.
- Variety — the different types of data generated every day that comes in a variety of formats like texts, audios, videos, csv, etc.
- Veracity — the uncertainties or the messiness of the data. With different forms of big data, it gets difficult to control the accuracy and quality. The volume often makes up the core reason behind the lack of accuracy and quality of the data.
- Value — having access to big data 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 of data 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 data tuples 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 stored data in a single platform, disclosing a new completely method to analytics.
Note: Reduce jobs are performed only after the execution of Map jobs.
4. What is Hadoop and mention the key components of Hadoop?
Apache Hadoop is the best solution for ‘Big Data’ problem. Hadoop is an open source Apache framework written in Java, which offers different tools and services to process and analyze big data, and helps to draw effective business decisions.
The main components of Hadoop are:
- YARN — processing framework (NodeManager, ResourceManager)
- HDFS — Storage unit (DataNode, Namenode)
5. List the differences between Hadoop 1.x and Hadoop 2.x.
In Hadoop 1.x, NameNode is the single point of failure (SPOF).
In Hadoop 2.x, there are two NameNodes i.e. Active NameNode and Passive NameNode. If in any case, the Active NameNode gets failed, then the Passive NameNode will take the charge. In Hadoop 2.x, YARN offers a central resource manager that allows running multiple applications on Hadoop.