Big data analytics has only recently begun. With more businesses relying on big data to run their operations, the demand for talent is at an all-time high. What does this imply for you? If you want to work in any of the big data positions, it only means more opportunities. You can train to be a Data Analyst, Data Scientist, Database Administrator, Big Data Engineer, Hadoop Big Data Engineer, and other positions. In this article, we will go over the top 50 Big Data interview questions.
When you're on a Big Data interview, the interviewer will ask you a few basic questions. Basic knowledge is necessary regardless of whether you are new or experienced in the big data field. So, let's go over some commonly asked basic, intermediate, and advanced big data interview questions and answers to help you ace your next big data interview.
Let's get started!
Big Data is a term that refers to complex and large datasets. A relational database cannot handle large amounts of data, which is why special tools and methods are used to perform operations on large amounts of data. Big data enables businesses to better understand their operations and derive meaningful information from unstructured and raw data collected on a regular basis. Big data also enables businesses to make better data-driven business decisions.
The five Vs of Big Data are as follows:
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Big data and Hadoop are nearly synonymous terms. With the rise of big data came the rise of Hadoop, a framework that specializes in big data operations. Professionals can use the framework to analyze big data and assist businesses in making decisions.
Big data analysis is becoming increasingly important for businesses. It assists businesses in differentiating themselves from competitors and increasing revenue. Big data analytics, through predictive analytics, provides businesses with customized suggestions. Furthermore, big data allows enterprises to announce new products based on customer needs and preferences.As a result of these factors, businesses earn so much revenue, and as a result, businesses use big data analytics. Implementing big data analytics can result in a significant increase in revenue of 5-20%. Walmart, LinkedIn, Facebook, Twitter, and Bank of America are just a few examples of well-known companies that use big data analytics to boost revenue.
The two primary components of HDFS are-
NameNode – This is the master node responsible for processing metadata information for data blocks within the HDFS DataNode/Slave node – This is the node that acts as a slave node, storing data for processing and use by the NameNode
In addition to serving client requests, the NameNode performs one of the two roles listed below –
CheckpointNode – It runs on a different host than the NameNode BackupNode – It is a read-only NameNode that contains file system metadata but not block locations.
YARN's two main components are–
ResourceManager– This component receives processing requests and allocates them to the appropriate NodeManagers based on the processing needs.
NodeManager– It is in charge of carrying out tasks on each and every Data Node.
Because data analysis has become one of the most important business parameters, businesses are dealing with massive amounts of structured, unstructured, and semi-structured data. Analyzing unstructured data is difficult, which is where Hadoop comes in handy with its capabilities.
The acronym fsck stands for File System Check. It is a command that is used by HDFS. This command is used to check for inconsistencies and problems in the file. For example, if a file has any missing blocks, HDFS is notified via this command.
The primary distinctions between NAS (Network-attached storage) and HDFS –
HDFS is run on a cluster of machines, whereas NAS is run on a single machine. As a result, data redundancy is a common problem in HDFS. In the case of NAS, however, the replication protocol is distinct. As a result, the likelihood of data redundancy is much lower.In the case of HDFS, data is stored as data blocks on local drives. It is stored in dedicated hardware in the case of NAS.
$ hdfs namenode -format
The three steps for deploying a Big Data Solution are as follows –
Data ingestion, or the extraction of data from various sources, is the first step in deploying a big data solution. The data source could be a CRM like Salesforce, an ERP like SAP, an RDBMS like MySQL, or any other log files, documents, social media feeds, and so on. Data can be ingested in two ways: batch jobs or real-time streaming. After that, the extracted data is saved in HDFS.
Following data ingestion, the extracted data must be stored. The data will be stored in either HDFS or a NoSQL database (i.e. HBase). HDFS storage is ideal for sequential access, whereas HBase is ideal for random read/write access.
Data processing is the final step in deploying a big data solution. The data is processed using one of the processing frameworks such as Spark, MapReduce, Pig, and so on.
Because Big Data provides a business with a competitive advantage over its competitors, a business can decide to tap the potential of Big Data based on its needs and streamline the various business activities based on its goals.As a result, the approaches to dealing with Big Data must be determined based on your business requirements and available budgetary resources.
First, you must determine the nature of your current business concerns. What questions do you want your data to answer? What are your business goals, and how do you intend to achieve them?In terms of approaches to Big Data processing, there are two options: They are processing in batches and Stream.
You can process Big Data in batches daily or after a set period of time, depending on your business needs. If your business requires it, you can process it in a timely manner every hour or every 15 seconds or so.
It all depends on your business goals and the strategies you employ.
There are several Big Data platforms available. Some of these are open source, while others require a license.
Hadoop is the most popular open-source Big Data platform. The other option is to use HPCC. HPCC is an abbreviation for High-Performance Computing Cluster.
We have licensed Big Data platform offerings from Cloudera (CDH), Hortonworks (HDP), MapR (MDP), and others. (Hortonworks is now part of Cloudera.)
We have tools like Storm for stream processing.
These are followed by energy and utilities, media and entertainment, government, logistics, and telecommunications, among other things.
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There are numerous Big Data tools on the market today. Some offer storage and processing services.
Some only offer storage and various APIs for processing, while others offer analytical services, and so on. Hadoop, Spark, HPCC, CDH, and other Big Data tools are the most widely used.
Hadoop was most likely the first open-source Big Data platform. It is highly scalable and runs on standard hardware. It includes the Hadoop Distributed File System (HDFS). It has the ability to store a large amount of unstructured data in a distributed fashion.
MapReduce, a data processing framework, is also included in Hadoop. It performs data processing in a highly parallel manner.
The processing time is drastically reduced for large amounts of data. There are numerous APIs and other tools that can be integrated with Hadoop, extending its usefulness, improving its capability, and making it more suitable for Big Data.
The Hadoop Framework allows the user to create and test distributions.
It's indeed fault-tolerant and provided in the following section across the cluster of machines automatically. It makes extensive use of parallelism. Hadoop does not rely on the underlying hardware to provide high availability and fault tolerance.
It provides such assistance at the application layer. We can add or remove nodes based on our needs. There is no need for you to make any changes to the application.
There are numerous factors to consider when making a business decision. A single bad decision can devastate an entire company. A Big Data set contains a wealth of information that, when used wisely, can greatly benefit a business.
It has the potential to transform any business that is willing to take advantage of it.
It is riddled with patterns, trends, and value. When this information is discovered, it can assist any business in making decisions based on actual data rather than human instinct. It aids in the development of various marketing, production, and inventory management strategies.
It has the potential to raise efficiency at all levels while significantly lowering overall costs.
A company that does not capitalize on the potential of Big Data may miss out on an opportunity and fall behind its competitors. It may make some mistakes if it fails to consider market and customer concerns. Because Big Data can provide valuable feedback and market thoughts, it can greatly assist a business in making wise, correct, and timely decisions and providing excellent business insights.
Data science encompasses a wide range of activities that involve the analysis of Big Data, the discovery of patterns and trends in data, the interpretation of statistical terms, and the prediction of future trends. Big Data is only one component of Data Science. Though Data Science is a broad term with many applications in business, it is nothing without Big Data.
All of our activities in Data Science are based on Big Data. As a result, Big Data and Data Science are inextricably linked and cannot be viewed in isolation.
There are numerous Big Data extraction tools available. Flume, Kafka, Nifi, Sqoop, Chukwa, Talend, Scriptella, Morphlines, and so on. Apart from data extraction, these tools also help with data modification and formatting.
There are several methods for extracting Big Data:
There are many other issues that must be addressed as well. The source and destination systems may use different I/O formats, protocols, scalability, security concerns, and so on. As a result, data extraction and storage must be handled appropriately.
Open source tools: Open source tools may be more appropriate for users on a tight budget.
They are expected to have an adequate knowledge base as well as the necessary supporting infrastructure. Some vendors provide open source versions of their tools that are light or limited in scope.
There are several languages available for querying Big Data. Some of these programming languages are functional, dataflow, declarative, or imperative. Big Data querying is frequently fraught with difficulties. As an example:
A model that really is overfit to the data is referred to as overfitting. It's a blunder in the modeling. It happens when a modeling function is too closely fitted to a small data set. The model is made too complex in this case to explain the peculiarity or individuality in the data under consideration.
Overfitting reduces the predictability of such models. The ability of such models to generalize is also harmed. Such models typically fail when applied to outside data, i.e. data that was not included in the sample data.
There are several methods for avoiding overfitting. They are as follows:
Missing values are the values which are not present in a single column. When we do not address the missing values, users may end up with incorrect data and, as a consequence, incorrect results. So, already when we handle Big Data, we must correctly treat the incomplete data in order to find the accurate sample. There are several approaches to dealing with missing values.
We can either discard the data or use data imputation to replace it.
If indeed the number of missing values is limited, it is normal practice to depart it alone. If the number of cases exceeds a certain threshold, data imputation is performed.
In facts and figures, there are several techniques for estimating so-called missing values:
We must integrate various data sources and systems in Big Data integration. Policies governing data collection, extraction, storage, and processing are subject to change. Different data points have different formats, architectures, tools and technologies, data transfer protocols, and so on. So, deciding to capture and use Big Data for your business will entail integrating these various data points, making some changes to the formats, usage, and security, among other things. It will have an effect on the day-to-day operations of the company.
Before proceeding with the integration process, several issues in Big Data integration must be addressed. Some of them are:
Many businesses have most likely already deployed their IT infrastructures based on their needs. When deciding to implement Big Data integration, businesses must rethink their IT strategies and make the necessary capital investments.
So, initially, there is reluctance in the organization to plan for Big Data adoption because it requires drastic changes at various levels.
Historically, information is stored in storage facilities in several enterprises. Because these data silos have complex configurations and formats, integrating them is a difficult task.
As a result, when we plan for Big Data integration, we should focus on the long-term requirements of the overall Big Data infrastructure rather than just the current integration needs.
In Big Data Graph Analytics, we attempt to model the given problem into a graph database and then perform analysis on that graph to obtain the necessary answers to our questions. Graph analytics can be used in a variety of ways, including:
Graph analytics can be used to detect fraud, financial crimes, identify social media influencers, optimize routes and networks, and so on.
In the early stages of Big Data, it's only available to large corporations. Small businesses were unable to use Big Data technologies because they were prohibitively expensive. However, with the rise of cloud and related technologies, even small businesses are recognizing the value of big data and capitalizing on it.
Predictive analytics is being used by an increasing number of businesses to drive sales and growth. The number of devices connected to the internet is also increasing. As a result, a massive quantity of data has been generated, that also includes insights that, when tapped, can be a huge benefit to businesses. The trend now is to use machine learning and AI to gain an advantage and stay competitive in the market.
The pattern is currently shifting away from on-premise handling and toward online/cloud processing. It relieves businesses of the need to make large investments in the future. They can now use the most up-to-date technologies and tools at a low/affordable cost. Because of these cost-per-usage trends, even small businesses can now gain access to Big Data Tools and Technologies, growing efficiency improvements throughout all levels.
Data preparation entails gathering, incorporating, preparing, and organizing data in order to evaluate it for structures, patterns, and deep insight. Preprocessing, cleansing, validating, and transforming Big Data are all required. The necessary data is gathered from various sources for this purpose. One of the primary goals of data preparation is to ensure that data being analyzed is precise and reliable. This is due to the fact that only accurate data will yield valid results.
Once information is collected, it is insufficient. It may contain some missing values, outliers, and so on. Data preparation is the most important and time-consuming activity in any Big Data project. Only good data can yield good results. The majority of the time, data is stored in separate databases in silos. It is also available in a variety of formats. As a result, it must be reconciled. There are five D's associated with the data preparation process. They are as follows:
The data preprocessing system is clearly automated. In data preprocessing, machine learning techniques could be used, such as stuffing null values, reassigning fields, maintaining accuracy, expelling redundant information, and so on. Data preprocessing, converting variables, removing the noise, information collection, data enrichment, data collection procedure and designing, and other terms are used to describe the process of information preparation. These phrases refer to the different processes or actions that take place during the data preparation phase.
It can be seen that the time cost of data preparation is usually longer than the time needed for data analysis.
However the techniques used only for data preparation seem to be streamlined, it takes a long time to process the data because the amount of information is indeed very large and continues to grow.
Dimensionality reduction refers to reducing the number of dimensions or variables taken into account. A large number of variables are present in Big Data. The majority of the time, some of these variables are linked. As a result, there is always the option of selecting only the major/distinctive variables that have a significant impact on the outcome. These variables are also known as Principal Components.
Dimensionality Reduction can be accomplished in two ways:
The following are the various methods for performing 'Feature Selection':
In 'Feature Extraction,' we reduce data from a 'high dimensional space' to a 'lower-dimensional space' with fewer dimensions. The 'Dimensionality Reduction' process can be linear or nonlinear. Dimensionality Reduction employs a number of techniques.
Data governance refers to the overall management of data, including its accessibility, decency, functionality, safety, and so on. A data governance council, well-defined procedures, and an effective plan for implementing those procedures and practices are required for effective data governance.
Whenever the integrity and trustworthiness of the given data are ensured, we get the expected business benefits from that data. Data governance is becoming increasingly critical and important as business depends more and more on data to make business decisions.
To have good information governance in position, numerous methods and steps must be implemented.
We make sure integrity of data, continuity, precision, availability, and reliability by integrating information governance. The definition of information privacy is regarded as the first step throughout data governance. The various processes for data storage, fallback, documentation, security, and so on must then be defined. Data access and permission rules and processes must be defined. It is necessary to define a policy platform and audit control mechanisms for ensuring compliance with various regulatory requirements and organizational policies.
A Big Data Maturity Model could also be used to track an organisation ’s objectives as it enters the world of Big Data. Also it assists in determining weak points and regions that require any additional attention in order to fit into the Data Analysis arena. A Big Data Maturity Model provides guidance on how an institution can end up making the most of its Big Data in order to reap the expected benefits. Use of the Big Data Maturity Model also implies that the more fully grown a model, the more the advantages a company can anticipate. It also aids in lowering overall operational costs.
Yes, there are various Big Data Maturity Model categories. It is generally divided into three levels:
The following are the criteria for evaluating Big Data Maturity Model:
We assess these Big Data Maturity Models by taking into account the various aspects of the business. These characteristics are as follows:
The Cloud is an excellent choice for Big Data Development. It will assist businesses in increasing operational efficiencies with a low initial investment. They just have to charge customers for the amenities they use. Moreover, they have the option to upgrade or downsize the amenities in response to changing business requirements.
In the above blog post we had prepared the curated list of big data interview questions for all the levels.Those interview questions will definitely help the learner to gain good knowledge in the subject and face the interview. Moreover if you find any useful question not covered here, please drop them in the comments section, we will definitely consider and add it in the above list.
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