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Big data is the fuel powering the success of many businesses today. There are hardly any companies today that are not using the power of big data and analytics to score wins in marketing, HR, production, and even operations. This implies that there is an increased requirement for Big Data engineers. With the competition being high, all recruiters want the best Big Data engineers. Thus, it is not easy to clear the Big Data engineer interview questions. It follows that before you go for your Big Data interview, it will be nice to prepare important Big Data engineer interview questions. In this docket, we have collated the best Big Data interview questions and answers for you.
Whether you are a Big Data engineer or a recruiter, you will find some use of these Big Data engineer interview questions.
Mention the big data processing techniques.
This question makes a frequent appearance across Big Data engineer interview questions.
The following are the techniques of big data processing:
The above methods help in processing vast amounts of data. When batches of big data are processed offline, the process happens at full scale and even helps tackle random business intelligence issues. When big data is processed using real-time streams of data, the most recent data slices are used to profile data and pick outliers, expose impostor transactions, monitor for safety precautions, etc. This becomes even more challenging when large data sets need to be processed in real-time. It’s because very large data sets must be analyzed within seconds. High parallelism must be used to process data to achieve this.
Talk about MapReduce in Hadoop.
MapReduce in Hadoop is basically a software framework that helps in processing large amounts of data. MapReduce functions as the main component when processing data using the Hadoop framework. The input data is split into many parts and the program is run on all data components simultaneously. MapReduce performs two tasks - map operation that transforms any given set of data into a diverse set in which the individual elements are segregated into tuples and the reduce operation that consolidates these tuples as per the key and later modifies the key value.
Define HDFS and YARN, and talk about their respective components.
While not so tough, this is a quintessential Big Data engineer interview question. HDFS or Hadoop Distributed File System helps one access data from different Hadoop clusters. Different Hadoop clusters would refer to the different computers that work together. When there are petabytes and zettabytes of data, HDFS comes in handy as a tool to analyze such large volumes of data. There are two main components of HDFS:
YARN or Yet Another Resource Negotiator is a resource manager that helps in monitoring and managing workloads, maintaining multi-tenant environments, managing high-availability features in Hadoop, and implementing security controls. There are two main components of YARN:
What is the purpose of the JPS command in Hadoop?
JPS stands for Java Virtual Machine Process Status. The JPS command helps in checking whether certain daemons are up or not. One can see all processes based on Java using this command. To check all the operating nodes of a host, the JPS command must be run from the root.
How do you deploy Big Data solutions?
The process for deploying Big Data solutions is as follows:
How is NFS different from HDFS?
The difference between NFS and HDFS is as follows:
NFS or Network File System allows the client to access its files over the network. It’s an open standard file system. Thus, it is easy to implement this file system. While the data is collected on the main system, all the computers on that network can access that data as if it were stored on their local system. The main issue with this file system is that the storage is dependent on the amount of space available on the main system. Moreover, if the main system goes down, all or some of the data may be lost.
HDFS or Hadoop Distributed File System is a distributed file system. This means that all the data is distributed and stored among different computers connected to the network. The large data sets run on commodity hardware. This system is used when we need to enlarge a single Apache Hadoop cluster into several hundred or thousand nodes. It helps in storing Big Data and enables faster data transactions. This system stores multiple replicas of the data files and hence, it can withstand faults in the system.
What are the 5 Vs in Big Data?
The 5 Vs are the five characteristics of Big Data. They are as follows:
What are the different Big Data processing techniques?
There are six main types of Big Data processing techniques.
A/B testing: In this method, a control group of data is compared with several test groups. This helps in identifying what changes or treatments can help improve the objective variable. For example, for an e-commerce site, what kinds of copy, images, and layout might give an impetus to the conversion rates. Big Data analytics can help in this case, however, the data sizes must be big enough to get meaningful differences to effect change.
Data integration and data fusion: This method involves combining techniques for analyzing and integrating data from multiple sources. This method is helpful as it gives more accurate results and insights when compared to getting insights based on a single data source.
Data mining: This is a common tool in Big Data analytics. In this method, statistical and machine learning models within database management systems are combined to extract and extrapolate patterns from large data sets.
Machine learning: Machine learning is an artificial intelligence technique that helps in data analysis. In machine learning, data sets are used for training computer algorithms for producing assumptions and predictions that are hitherto impossible for humans to attain.
Natural language processing or NLP: NLP is based on computer science, artificial intelligence, and linguistics and uses computer algorithms to understand human language to derive patterns.
Statistics: One of the oldest methods of processing data, statistical models help in collecting, organizing, and interpreting data from surveys and experiments.
Talk about the different features of Hadoop.
The different features of Hadoop are as follows:
What are the Port Numbers for NameNode, Task Tracker, and Job Tracker?
The Port Number for these are as follows:
The above set of Big Data engineer interview questions will help you with the technical part of your Big Data engineer interview. If you want to ensure that you score well in your Big Data engineer interview, then you must prepare these and other similar Big Data engineer interview questions. However, your Big Data engineer interview will have technical and soft skills questions too. Companies and recruiters want to conduct Big Data engineer interviews to get Big Data engineers who are assets for the entire team. Asking soft skills questions helps recruiters in determining whether you will be such an asset or not. Thus, while preparing for your Big Data engineer interview, focus on preparing both technical and soft skills questions. Practicing with a friend or colleague can often help in preparing for soft skills questions.
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