The twenty-first-century era is marked by the emergence of data, big data, and the internet of things changing the technological landscape. The demand for Big Data experts is also on the rise with numerous companies inclining toward Big data. So what does this mean for you? The rapid development of big data translates into more prospects for you if you are someone who is interested in its expansion and is seeking chances to land a job in one of the positions.
You should be well-prepared for the big data interview to offer your career a boost. We have formulated the most relevant big data interview questions and answers to help you prepare well for your interview. Prior to getting started, it's critical to realize that the interview is a location where you and the interviewer engage solely to understand one another and not the other way around. As a result, you don't need to hide anything; simply be sincere in your responses to the questions. Feel free to ask the interviewer questions if you're unclear or need more details. Always be truthful in your response, and when necessary, ask questions.
The top Big Data interview questions and answers provided below will guide you to the nuances of what a Big Data interview is like. We will classify the big data interview questions and answers into different levels to match the background and expertise level of the prospective candidates. Let us get started with it.
Every time you attend a Big Data interview, the interviewer might pose some simple inquiries. No matter your level of familiarity with big data, the fundamentals are still necessary. In order to succeed let's go through some often-asked fundamental big data interview questions and answers.
Big Data is a term linked with complicated and huge datasets. Big data operations require specialized tools and techniques because a relational database cannot handle such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and assists them in extracting valuable information from the unstructured and raw data that is routinely gathered. Big data enables businesses to make more informed business decisions.
The following are the Five Vs fundamental to Big Data:
The terms "big data" and "Hadoop" are essentially interchangeable. Hadoop, a framework that focuses on big data operations, gained popularity along with the growth of big data. Professionals can use the framework to evaluate big data and assist organizations in decision-making.
The importance of big data analysis to organizations cannot be overstated. It aids companies in standing out from the competition and boosting sales. Big data analytics offers organizations individualized advice and suggestions through predictive analytics. Additionally, big data analytics enables companies to release new products in accordance with customer preferences and needs. Due to the increased money these elements bring in, organizations are turning to big data analytics. By integrating big data analytics, businesses may see a considerable rise in revenue of 5-20%. Walmart, LinkedIn, Facebook, Twitter, Bank of America, and other well-known firms are some examples of those adopting big data analytics to boost their sales.
The steps that are taken to install a big data solution are as follows:
Data Ingestion: Data Ingestion or in other words the extraction of data from diverse sources, is the initial step in the deployment of a big data solution. The source of data could be anything like the following:
Either batch jobs or real-time streams can be used for the ingestion of the data. The obtained information is then kept in HDFS.
Information Storage: The next step after data input is to store the extracted data. Either a NoSQL database or HDFS will be used to store the data (HBase). HBase is better for random read/write access, while HDFS storage is better for sequential access.
Information processing: Data processing is the last stage of deploying a big data solution. One of the processing frameworks, such as Spark, MapReduce, Pig, etc., is used to process the data.
The amount of organized, semi-structured, and unstructured data that make up big data makes it a challenging undertaking to analyze and handle. A device or piece of technology was required to aid in the quick processing of the data. Hadoop is therefore utilized as a result of its processing and storage capabilities.
Hadoop is also an open-source piece of software. It is also advantageous for the company's solution given its cost considerations.
Its widespread use in recent years is mostly due to the framework's ability to disperse the processing of large data sets via cross-computer clusters employing straightforward programming paradigms.
An open-source framework called Hadoop is designed to store and handle large amounts of data in a distributed fashion. The core elements of Hadoop are:
HDFS consists of two primary components:
In addition to fulfilling client requests, the NameNode performs one of the two roles listed below:
CheckpointNode runs on a different host than the NameNode.
BackupNode is a read-only NameNode that holds file system metadata information except for the block locations.
The YARN's two primary components are:
Fsck is an acronym for file system check. It is a command that HDFS employs. This command is used to check for discrepancies and determine whether the file has any issues. For instance, HDFS receives a notification from this command if any file blocks are missing.
Hadoop helps with both data storage and big data processing. It is the most dependable method for overcoming significant data obstacles. Some key characteristics of Hadoop include:
The key distinctions between HDFS and NAS (Network-attached storage) are as follows:
NAS is run on a single machine, whereas HDFS is run on a cluster of machines. Data redundancy is thus a typical problem in HDFS. On the other hand, NAS uses a different replication protocol. Data redundancy is therefore far less likely.
In the case of HDFS, data is kept as data blocks on local devices. It is kept on specialized hardware in the case of NAS.
$ hdfs namenode -format
The IT industry has been using data modeling as a business model for many years. The data model is a method for arriving at the diagram by thoroughly understanding the data in question. The process of visualizing the data allows business and technology experts to comprehend the data and comprehend how it will be used. The various Data Model Types can be described below. Consider them as a step up from a simple layout to a thorough representation of the database setup and final form:
In your big data interview, you will be asked a number of questions based on your prior experience if you have a lot of expertise working in the big data industry. Simply based on your experience or scenario-based, these questions may be. Prepare for your big data interview with these top questions and answers.
How to Proceed: The question is subjective, thus there isn't a right or wrong response because it relies on your prior experiences. The purpose of asking this question during a big data interview is to better understand your prior experience and determine whether you are a fit for the position.
A software platform called Hadoop MapReduce is used to process huge data quantities. It is the primary part of the Hadoop system for processing data. It separates the incoming data into different components and executes a program on each piece of data in parallel. The terms "MapReduce" refer to two distinct jobs. The first is the map operation, which turns a set of data into a varied collection of data in which individual components are separated into tuples. The key-based data tuples are combined via the reduction operation, which also affects the key's value.
A parallel distributed computation model called MapReduce was developed for large data sets. A MapReduce model has a reduced function that acts as a summary operation and a map function that performs filtering and sorting.
For selecting and requesting data from the Hadoop Distributed File System, MapReduce is a vital component of the Apache Hadoop open-source ecosystem (HDFS). Depending on the wide range of MapReduce algorithms available for selecting data, several types of queries may be run. MapReduce is also appropriate for iterative computation involving vast amounts of data that need parallel processing. This is due to the fact that it depicts a data flow rather than a process. The need to process all that data to make it useful increases as we produce and amass more advanced data.
Big data can be understood well by using the iterative, parallel processing programming approach of MapReduce.
A Reducer's primary methods are:
When you need to exchange files around all nodes in a Hadoop cluster, the MapReduce Framework's Distributed Cache is a crucial tool that you can use. These files can be simple properties files or jar files. Text files, zip files, jar files, and other small to medium-sized read-only files can all be cached and distributed across all Datanodes (worker-nodes) where MapReduce jobs are running thanks to Hadoop's MapReduce framework. Distributed Cache sends a local copy of the file to All Datanode.
A model that is tightly suited to the data, or when a modeling function is strongly fitted to a small data set, is typically referred to as overfitting. The predictability of such models decreases as a result of overfitting. When used outside of the sample data, this impact causes a loss in generalization ability.
There are many ways to prevent overfitting, some of which include:
Hadoop's ability to divide and conquer with a Zookeeper is its most notable method for solving huge data difficulties. The solution depends on using distributed and parallel processing techniques throughout the Hadoop cluster after the problem has been partitioned.
The insights and promptness required to make business decisions for large data challenges cannot be provided by interactive technologies. To address these massive data issues, distributed apps must be built in those circumstances. Hadoop uses Zookeeper to manage all the components of these distributed apps
The merits of hiring a Zookeeper can be listed as follows:
Simple distributed coordination process: In Zookeeper, the coordination between all nodes is simple.
Synchronization: Mutual exclusion and cooperation between server processes are examples of synchronization.
Ordered Messages: The zookeeper tracks communications with a number and indicates their order by stamping each update; as a result, messages are ordered here.
Serialization: Follow predetermined procedures to encode the data. Make sure your program functions consistently.
Reliable: The zookeeper is quite trustworthy. If there is an update, all the data is kept until it is transmitted.
Atomicity: No transaction is incomplete; data transmission either succeeds or fails.
Ephemeral znodes are those that are transient. Every time the creator client leaves the ZooKeeper server, it is destroyed. Assume, for instance, that client1 made eznode1. The eznode1 is destroyed once client1 shuts off of the ZooKeeper server.
The replication factor is 3 by default. On the same data node, there won't be any duplicate copies. The first two copies are typically found on the same rack, but the third copy is typically taken off the shelf. In order to ensure that one copy is always secure, regardless of what happens to the rack, it is recommended to set the replication factor to at least three.
The file system's default replication factor can be customized for each file and directory. Non-critical files can have their replication factor lowered, whereas critical files should have a high replication factor.
Robust: It is incredibly strong and simple to use. Additionally, the community supports it and contributes to it.
Full Load: In Sqoop, a table can be fully loaded with just one command. Additionally, many tables may be loaded simultaneously.
Incremental Load: The capability of incremental loading is also supported. With Sqoop, the table can also be loaded in segments whenever it is modified.
Parallel import/export: The YARN framework handles data import and export. Additionally, it offers fault tolerance.
SQL query output import: It enables us to import SQL query output into the Hadoop Distributed File System.
Hadoop fs -copyFromLocal [source][destination] is the command used to copy data from the Local system to HDFS.
According to the values of the partitioned columns, partitioning in Hive generally involves logically dividing tables into similar columns like date, city, and department. Following that, these partitions are further separated into buckets to give the data more structure that may be used for more effective searching.
Let's now use an instance of Hive to experiment with data partitioning. Take Table1 as an example. Client information from the table includes client id, name, department, and year of joining. Let's say we want to get information about every client that joined in 2014. The query then searches the entire table for the required information. The query processing time will be reduced if the client data is divided by year and saved in a distinct file.
Big data processing may result in the selection of only a small subset of the specific features that we are interested in because there may be a great amount of data present that is not needed at a given time. Feature selection is the process of just obtaining the necessary features from the Big data.
Methods for choosing features include:
Filters Method: In this approach to variable ranking, we simply take a feature's significance and utility into account.
Wrappers Method: The "induction algorithm" is employed in the "wrappers approach," which can be used to create a classifier.
Embedded Method: This technique combines the effectiveness of the Wrappers and Filters approaches.
To receive the output file of a sqoop import in a format other than.gz, use the -compress-codec argument.
Heterogeneity: The design of applications should take into account hardware devices, operating systems, networks, and programming languages to enable users to access services and run applications over a heterogeneous collection of computers and networks.
Transparency: Distributed system designers must do everything in their power to disguise the complexity of the system. Location, access, migration, relocation, and other terms related to transparency are only a few.
Openness: The ability of a system to be extended and reimplemented in different ways depends on its openness.
Security: Confidentiality, integrity, and availability must be considered by distributed system designers.
Scalability: A system is said to be scalable if it can handle an increase in users and available resources without experiencing a drastic decrease in performance.
Big Data's benefits include:
Productivity gains: According to a recent study, big data solutions like Hadoop and Spark are used by 59.9% of organizations to grow their sales. With today's big data tools, analysts can rapidly investigate, which increases their productivity. Additionally, firms can leverage the conclusions drawn from the study of big data to boost productivity across the board.
Cost savings: Big data analytics aids in cost savings for enterprises. The majority of businesses have used big data tools to improve operational efficiency and cut costs, and a small number of other businesses have begun doing the same. It's interesting to note that very few businesses choose cost reduction as their main objective for big data analytics, indicating that for most, this is just a very pleasant side benefit.
Fraud detection: In the financial services sector, employing big data analytics for fraud detection is the main goal. Big data analytics systems have the advantage of relying on machine learning, which makes them excellent at spotting patterns and anomalies. Because of this, banks and credit card issuers may be able to identify fraudulent purchases or stolen credit cards before the cardholder ever becomes aware of them.
More significant innovation: A few businesses have begun to invest in analytics with the explicit intent of disrupting their markets. The reason for this is that they can emerge victorious from that position with a few new items and services and quickly seize the market if they can glimpse the future of the market with the help of insights before their competitors.
Using Big data may have a few disadvantages as follows:
Talent shortage: For the past three years, the biggest problem with big data has been the lack of the necessary skill sets. Many businesses have trouble creating a data lake. Employing or training personnel will only significantly increase the cost, and it takes a long time to learn big data expertise.
Cybersecurity risks: Businesses that store large data, especially sensitive big data, will become attractive targets for cyberattacks. One of the biggest problems with big data is secure, and the biggest threat to data that businesses face is cybersecurity breaches.
Hardware requirements: The IT infrastructure required to support big data analytics is another crucial problem for enterprises. Costly to purchase and maintain are the storage space for the data, networking bandwidth for moving the data to and from analytics systems, and computing resources to carry out those analytics.
Data quality: One drawback of using big data was having to deal with issues with data quality. Data scientists and analysts must make sure that the data they are dealing with is correct, acceptable, and in the right format for analysis before businesses can use big data for analytics. While this slows the process, failure to address data quality issues could result in insights from analytics that are ineffective or even harmful.
There are numerous methods to ask an open-ended question.
Coding/programming: The most tried-and-true approach to converting unstructured data into a structured form is coding/programming. Programming is helpful because it provides independence, which may be used to alter the data's structure in any way imaginable. It is possible to employ a variety of programming languages, including Python, Java, etc.
Data/Business Applications: A lot of BI (Business Intelligence) tools allow for the drag-and-drop transformation of unstructured data into structured data. The fact that the majority of BI tools are paid for and require financial support should be taken into consideration before employing them. This is the best course of action for those who lack the expertise and qualifications required for Option 1.
The practice of cleaning and changing raw data before processing and analysis are known as data preparation. Prior to processing, this critical stage frequently entails reformatting data, making enhancements, and combining data sets to enrich data.
For data specialists or business users, data preparation is a never-ending effort. However, it is crucial to put data into context in order to gain insights and then be able to remove the skewed results discovered as a result of bad data quality.
For instance, the data-building process frequently entails improving source data, standardizing data formats, and/or removing outliers.
Data preparation steps include:
Data collection: Accurate data collection is the first step in the data preparation process.
This may come from an existing data catalog or may be added on the fly.
Data Discovery and Assessing: Each dataset must be identified after the data have been assembled.
Understanding the data and knowing what has to be done to make it useful in a particular context are the goals of this step. The difficult work of discovery can be accomplished with the aid of data visualization tools that guide people as they examine their data.
Data cleaning and verification: Although this phase takes the longest amount of time, it is the most crucial because it fills in any gaps in the data as well as removes any wrong information. Here, important responsibilities include:
Data transformation and enrichment: Updating data allows for the achievement of well-defined results or the improvement of data recognition by a wider audience. Adding to and integrating data with additional relevant information in order to gain deeper insights is referred to as "improving data."
Data Storage: Data can also be gathered or directed into a third-party application, such as a business intelligence tool-making technique, for processing and analysis as the last option.
The common input formats for Hadoop are listed below.
There are three operating modes for Apache Hadoop.
Standalone (Local) Mode: Hadoop by default operates in a single, non-distributed node local mode. This mode is tasked with the input and output operations by employing the local file system. This mode is used for debugging because HDFS is not supported in it. In this mode, configuration files don't require any special settings.
Pseudo-Distributed Mode: Similar to the Standalone manner, Hadoop operates in the pseudo-distributed mode on a single node. Each daemon operates in a separate Java process in this configuration. The same node serves as the Master and Slave nodes because it houses all of the daemons.
Fully-Distributed Mode: In the fully-distributed mode, each daemon runs on its own distinct node, resulting in the formation of a multi-node cluster. For Master and Slave nodes, there are various nodes.
In Hadoop, security is achieved using Kerberos. At a high level, utilizing Kerberos, there are three stages required to access a service. A server message exchange is required for each stage.
Authentication: The first stage entails the client's authentication with the authentication server, after which the client receives a time-stamped TGT (Ticket-Granting Ticket).
Authorization: The client uses the TGT they have received to ask the TGS for a service ticket at this phase (Ticket Granting Server).
Service Request: The final step to achieving security in Hadoop is to submit a service request. The client then uses a service ticket to log in to the server.
Commodity hardware is a low-cost system characterized by low quality and limited availability. RAM is a component of commodity hardware since it is used for a variety of tasks whose execution calls for RAM. Hadoop may be used on any commodity hardware and doesn't require supercomputers or high-end system configurations to run.
There are various distributed file systems, and they all function differently. While HDFS (Hadoop Distributed File System) is the most recently utilized and well-liked distributed file storage system, NFS (Network File System) is one of the oldest and most well-known ones for storing distributed files. The following are the primary distinctions between NFS and HDFS:
Data Size Support: While HDFS is particularly used for storing and processing Big data, NFS is known to process and store small data amount.
Data Storage: In NFS storage of data is made in single-dedicated hardware. However, in HDFS data blocks are split amongst the local devices of hardware.
Reliability: NFS is challenged with the issue of no reliability. In the event of machine failure, data will not be available. On the contrary, HDFC is reliable and is available even when there is machine failure.
Data Redundancy: With NFS there is no chance of data redundancy as it operates on a single machine however, in HDFS there is data redundancy which may be caused by replication protocol as it operates on a cluster of varied machines.
A Mapper's fundamental specifications are
The correct response is that all daemons must be stopped before they can be restarted. The script files used to start and stop Hadoop daemons are kept in the sbin directory, which is found in the Hadoop directory.
Use the /sbin/stop-all.sh command to stop all daemons, and then use the /sin/start-all.sh command to start them all running again.
The jps command is used to determine whether the Hadoop daemons are functioning correctly or not. The daemons running on a machine, such as Datanode, Namenode, NodeManager, ResourceManager, etc., are all displayed by this command.
To start or stop Hadoop daemons, CLASSPATH contains the appropriate folders that include jar files. Therefore, in order to start or stop Hadoop daemons, CLASSPATH must be configured.
However, it is not our practice to always configure CLASSPATH. Typically, the file /etc/hadoop/hadoop-env.sh contains the CLASSPATH directive. As a result, once Hadoop is running, the CLASSPATH is immediately loaded.
In Hadoop, there is no such thing as a NameNode without any data. If a NameNode exists, it either has data in it or it doesn't.
This is a result of NameNode's performance problem. Typically, a substantial amount of space is allotted to NameNode to store the large file's metadata. To make the most of space and save money, the metadata should come from a single file. It is a performance optimization problem when NameNode does not use the complete space for little files.
Blocks are stored as datasets in the Hadoop cluster's DataNodes via HDFS. A MapReduce job's own Mapper processes the blocks as it runs (Input Splits). The data must be transferred over the network from the DataNode to the Mapper DataNode if it is not already present at the node where the Mapper is running the task.
Currently, if a MapReduce task contains more than 100 Mappers and each Mapper attempts to copy the data from another DataNode in the cluster concurrently, it would result in major network congestion and be a significant performance issue for the entire system. Thus, having the data close to the computation, or what Hadoop refers to as "Data locality," is an efficient and affordable option. It aids in boosting the system's overall throughput.
There are three types of Data Locality
Data local: In this category, the mapper and the data are located on the same node. The most ideal situation is this one because the data is closest to hand.
Rack Local: In this case, the data nodes and the mapper are located on the same rack.
Different Rack: In this case, the mapper and the data are on separate racks.
Hadoop is used to analyze massive data as well as store vast data. Although the Distributed File System (DFS) can also store data, it lacks the following features:
It cannot tolerate faults.
Bandwidth determines how quickly data moves over a network.
Hadoop utilizes a particular file type called a Sequence file. A serialized key-value pair is used to store data in the sequence file.
The input format for reading sequence files is called sequencefileinputformat.
It is a tool used for concurrent data copying to and from Hadoop file systems with very huge amounts of data. Its distribution, error handling, recovery, and reporting are all impacted by MapReduce. A series of files and directories is expanded into a series of inputs to map jobs, each of which copies a specific subset of the files listed in the source list.
Numerous opportunities are opening up for big data specialists as the big data industry continues to grow. Your interview will go much smoother if you use this top Big Data interview Questions and answers set. However, we must not undervalue the value of certifications and intensive training. Get certified and add a certificate to your resume if you want to show off your skills to the interviewer during the big data interview.
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