Top Big Data engineer interview questions and answers for 2022

This question makes a frequent appearance across Big Data engineer interview questions.

The following are the techniques of big data processing:

• Processing of batches of big data
• Stream processing of big data
• Big data processing in real-time
• Map-reduce

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.

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.

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:

• NameNode: This is a master node that helps process information from the metadata of the data blocks found in the HDFS.
• DataNode: The DataNode does not process the data, instead it helps in storing the data to be processed by the NameNode.

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:

• ResourceManager: Upon receiving requests for processing, the ResourceManager processes these requests and allots its parts to different NodeManagers depending on the kinds of processing needs.
• NodeManager: All tasks on all DataNodes are executed by the NodeManager.

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.

The process for deploying Big Data solutions is as follows:

• Ingestion of data: The first part of the process is to collect and stream data from various sources such as log files, SQL databases, and social media files. The three main challenges of data ingestion are large table ingestion, the capture of change data, and the changes of Schema in the source.
• Storage of data: The second step is to store or load the data that has been extracted from various sources in HDFS or NoSQL by the HBase. Applications can easily access and process this stored data.
• Processing of data: The next and very important step is to process the data. MapReduce and Spark framework help in analyzing large scale - petabytes and zettabytes of data.
• Visualization and reporting: The last step of the process is perhaps the most important. Once the data has been analyzed, it is critical to present it in a digestible format for people to understand.

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.

The 5 Vs are the five characteristics of Big Data. They are as follows:

• Volume: The name Big Data is derived from the enormous amounts of data that are stored and processed. When the volume of data is very large, it is usually considered Big Data.
• Velocity: Big Data is also gathered at a very high speed. It is the speed at which Big Data flows in from sources such as networks, machines, mobile phones, and social media. There is enormous data flowing in continuously. So, if the speed is high, such large amounts of data can be collected and processed fast to get the desired results.
• Variety: The data flows continuously from different sources both external and internal to an enterprise. Thus, some of the data may be structured and some may be unstructured. There are also semi-structured categories of data.
• Veracity: Veracity refers to the truth of the data. Here it is important how consistent and accurate the data is. Since data comes from multiple sources, it is not always authentic. Also, data types are often disparate and thus, it may be difficult to derive consistent and logical conclusions at all times.
• Value: While copious data is gathered in Big Data, all of it doesn’t necessarily prove useful unless it can be processed to get meaningful insights. Thus, to drive value for businesses and companies, the data must be analyzed to present insights.

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.

The different features of Hadoop are as follows:

• Open Source: As an open-source platform, Hadoop offers the ability to rewrite or change the code as per user needs and analytics requirements.
• Scalability: Hadoop offers scalability by supporting adding hardware resources to the new nodes of network computers.
• Data recovery: Because Hadoop keeps duplicate data across multiple computers on the network, it is possible to recover data in case of any faults or failures.
• Data locality: In Hadoop, the data need not be moved for processing. Instead, the computation can take place where the data is, thereby speeding up the process.

The Port Number for these are as follows:

• The Port Number for NameNode is Port 50070
• The Port Number for Task Tracker is Port 50060
• The Port Number for Job Tracker is Port 50030