The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

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Big Data Market Size 2024-2028

The big data market size is forecast to increase by USD 508.73 billion at a CAGR of 21.46% between 2023 and 2028.

The market is experiencing significant growth due to the growth in data generation from various sources, including IoT platforms and digital transformation services. This data deluge presents opportunities for businesses to leverage advanced analytics tools for applications such as fraud detection and prevention, workforce analytics, and business intelligence. However, the increasing adoption of big data implementation also brings challenges, including the need for data security and privacy measures. Quantum computing and blockchain technology are emerging trends In the big data landscape, offering potential solutions to complex data processing and security issues. In healthcare analytics, data protection regulations are driving the need for secure data management and sharing.
Additionally, supply chain optimization is another area where big data can bring significant value, enabling real-time monitoring and predictive analytics. Overall, the market is poised for continued growth, driven by the need to extract valuable insights from the vast amounts of data being generated.

What will be the Size of the Big Data Market During the Forecast Period?

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The market is experiencing growth as businesses increasingly leverage information from vast datasets to drive strategic decision-making, enhance customer experiences, and improve operational efficiency. The digital revolution has led to an exponential increase in data creation, fueling demand for advanced analytics capabilities, real-time processing, and data protection and privacy solutions. Hardware and software companies offer on-premise and cloud-based systems to accommodate various industry needs, including customer analytics in retail and e-commerce, supply chain analytics in manufacturing, marketing analytics, pricing analytics, spatial analytics, workforce analytics, risk and credit analytics, transportation analytics, healthcare, energy and utilities, and IT and telecom. Big data applications span numerous sectors, enabling organizations to gain valuable insights from their data to optimize operations, mitigate risks, and innovate new products and services.

How is this Big Data Industry segmented and which is the largest segment?

The big data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises big data software solutions involve the installation of hardware and software by the end-user, granting them complete control over the system. Despite the high upfront costs, on-premises solutions offer advantages such as full ownership and operational efficiency. In contrast, cloud-based solutions require recurring monthly payments and involve data storage on companies' servers, increasing security concerns. Advanced analytics, real-time processing, and integrated analytics are key features driving the market. Data creation from digital transformation, customer experiences, and various industries like retail, healthcare, and finance, fuel the demand for scalable infrastructure and user-friendly interfaces. Technologies such as quantum computing, blockchain, AI-driven analytics platforms, and automation are transforming business intelligence solutions.

Ensuring data protection and privacy, accessibility, and seamless data transactions are crucial in this data-driven era. Key technologies include distributed computing, visualization tools, and social media. Target audiences range from decision-makers to various industries, including transportation, energy, and consumer engagement.

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The On-premises segment was valued at USD 86.53 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is experiencing significant growth due to digital transformation initiatives by enterprises in sectors such as healthcare, retail

As of March 2024, there were a reported 5,381 data centers in the United States, the most of any country worldwide. A further 521 were located in Germany, while 514 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These centers can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

Please cite the following paper when using this dataset:N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007AbstractThe exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today’s living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 Tweets about exoskeletons that were posted in a 5-year period from 21 May 2017 to 21 May 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Question Paper Solutions of chapter Introduction of Big Data of Big Data Analysis, 8th Semester , Computer Science and Engineering

In 2022, China's big data industry grew by almost 18 percent compared to the previous year, exceeding a market size of 1.5 trillion yuan. The Chinese government has plans to transform the country into a global technology leader and big data is one important vector in this development.

Learn how defense and intelligence users can leverage ArcGIS GeoEvent Server and ArcGIS GeoAnalytics Server to connect to real-time data feeds and run analytics on the stored data. From tracking units in the field to analyzing intelligence feeds and weather, ArcGIS GeoEvent Server enables users to stay current on what is happening. When you want to analyze massive amounts of stored track and report data, ArcGIS GeoAnalytics Server uses distributed computing to return spatiotemporal insight helping you make better planning decisions.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

The need for advanced analytical approaches to provide HPDA solutions is driving the market growth of High Performance Data Analytics (HPDA). According to the analyst from Verified Market Research, The High Performance Data Analytics (HPDA) Market is estimated to reach a valuation of USD 597.06 Billion over the forecast period 2031, by subjugating around USD 113.23 Billion in 2023.

The adoption of an open-source framework for big data analytics is driving market growth. This surge in demand enables the market to grow at a CAGR of 23.1% from 2024 to 2031.

High Performance Data Analytics (HPDA) Market: Definition/ Overview

HPDA refers to big data analytics that uses High-Performance Computing (HPC) techniques. Big data analytics has always relied on high-performance computing (HPC), but as data grows exponentially, new forms of high-performance computing will be required to access previously unimaginable volumes of data. The combination of big data analytics and high-performance computing is called “high-performance data analytics.” High-performance data analytics is the process of quickly finding insights from large data sets by running powerful analytical tools in parallel on high-performance computing systems.

Furthermore, high-performance data analytics infrastructure is a rapidly expanding market for government and commercial organizations that need to combine high-performance computing with data-intensive analysis. For complex modeling and simulations, big data analytics techniques like Hadoop and Spark have long required high-performance computing, which they lack.

In 2021, the internet accounted for the largest share of big data applications in China. In the same year, the big data industry size amounted to almost 1.3 trillion yuan. Big data is the backbone of China's technology industry.

The Big Data market for Telecommunications and Media & Entertainment is experiencing robust growth, driven by the increasing volume of data generated by these sectors and the need for advanced analytics to extract valuable insights. This market is projected to reach a significant size, with a Compound Annual Growth Rate (CAGR) fueling expansion. Let's assume, based on typical industry growth rates for related sectors, a conservative market size of $50 billion in 2025 and a CAGR of 15% for the forecast period (2025-2033). This implies substantial expansion, reaching an estimated market value exceeding $150 billion by 2033. This growth is propelled by several key factors. Firstly, the proliferation of connected devices and the rise of streaming services generate massive datasets requiring sophisticated Big Data solutions for effective management and analysis. Secondly, the need for personalized experiences, targeted advertising, and fraud detection in the telecommunications and media sectors are driving demand for advanced analytics capabilities offered by Big Data technologies. Furthermore, the increasing adoption of cloud-based deployment models and the emergence of innovative technologies like AI and machine learning are further accelerating market growth. However, challenges remain. Data security and privacy concerns continue to be significant hurdles, requiring robust security measures and compliance with evolving regulations. Integration complexities across diverse data sources and the need for skilled professionals capable of managing and interpreting Big Data also pose restraints to market expansion. Nevertheless, the overall trend points towards continued, albeit potentially moderated, growth, with the market segmented by application (Telecommunications, Media & Entertainment), type (Software, Hardware), and deployment models (cloud, on-premises, hybrid). The competitive landscape is crowded, with major players such as Microsoft, Google, AWS, IBM, and others vying for market share through innovative solutions and strategic partnerships. The regional breakdown reveals strong growth across North America and Europe, while Asia-Pacific is emerging as a significant growth market, driven by increasing digital adoption and technological advancements.

ABSTRACT A lot amounts of data i.e information that related to make wonders with work is called as 'BIG DATA' Last two decades big data treated as a special interest and had a lot potentiality because of hidden features in it. To generate, store, and analyze big data with an aim to improve the services they provide in multiple no of small & large scale industries. As we are considering the health care industry for this big data is providing multiple opportunities like records of patients, inflow & outflow of the hospitals. It also generates a significant portion of big data relevant to public healthcare in biomedical research. In order to derive meaningful information analysis & proper management of data is required. In the haystack seeking solution in big data will be quickly analyzable just like finding a needle. in big data analysis various challenges associated with each step of handling big data surpassed by using high-end computing solutions. for improving public health healthcare providers provide relevant solutions & to systematically generate and analyze big data requirements to be fully loaded with efficient infrastructure. in big data can change the game by opening new avenues for modern healthcare with an efficient management, analysis, and interpretation. vigorous instructions are given by the various industries like public sectors followed by healthcare for the betterment of services and as well as financial upgrades. by taking the revolution in healthcare industry we can accommodate personnel medicine included by therapies in strong integration manner. Keywords: Healthcare, Biomedical Research, Big Data Analytics, Internet of Things, Personalized Medicine, Quantum Computing Cite this Article: Krishnachaitanya.Katkam and Harsh Lohiya, Patient Centric Management Analysis and Future Prospects in Big Data Healthcare, International Journal of Computer Engineering and Technology (IJCET), 13(3), 2022, pp. 76-86.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals’ private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.
Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The Web 2.0 Data Center market is projected to reach a value of USD XX million by 2033, growing at a CAGR of XX% from 2023 to 2033. The market is driven by the rising demand for cloud-based services, big data analytics, and the proliferation of Internet of Things (IoT) devices. The increasing adoption of artificial intelligence (AI) and machine learning (ML) applications is also contributing to the growth of the market. The market is segmented by application into cloud computing, big data analytics, and content delivery networks. The cloud computing segment is expected to hold the largest share of the market throughout the forecast period due to the growing adoption of cloud-based services by businesses of all sizes. The big data analytics segment is expected to grow at the highest CAGR during the forecast period due to the increasing demand for data analytics solutions to gain insights from large volumes of data. The content delivery networks segment is also expected to grow at a significant rate due to the rising demand for online video and audio streaming services.

The global Big Data Security market was valued at USD 11.79 billion in 2025 and is projected to grow at a CAGR of 14.81% from 2025 to 2033. Increasing adoption of cloud computing and proliferation of big data across various industry verticals are key factors driving the growth of the market. Growing concerns regarding data breaches and stringent government regulations to protect sensitive data are also fueling the demand for big data security solutions. Key trends shaping the market include the emergence of artificial intelligence (AI) and machine learning (ML) in big data security, increasing adoption of cloud-based security solutions, and growing focus on data privacy and compliance. The market is highly competitive, with established players such as Symantec Corporation, Fortinet, Check Point Software Technologies Ltd., IBM, and Hewlett Packard Enterprise (HPE) dominating the landscape. These companies are investing heavily in research and development to enhance their product offerings and strengthen their market position. Regional markets such as North America and Europe are expected to remain dominant throughout the forecast period due to the presence of well-established IT infrastructure and stringent data protection regulations. Asia Pacific is also expected to witness significant growth as businesses in the region increasingly adopt big data technologies and prioritize data security. Recent developments include: March 2024, On behalf of its clients, Telefónica Tech UK&I is pleased to announce the introduction of the cutting-edge cyber security services brand known as "NextDefense." This brand will assist customers in achieving a safe digital future. The term "NextDefense" refers to the next generation of Managed Security Services (MSS), which Telefónica Tech provides from its global network of Security Operations Centers (SOCs). This new generation of MSS incorporates advanced capabilities that are in line with the shifting threat landscape, emerging technologies, and the requirement for proactive security., The 'NextDefense' solution, which Telefónica Tech now provides in the United Kingdom and Ireland, is equipped with proprietary threat information, cutting-edge technology, and automation-driven standardized processes. This is made possible by Telefónica Tech's significant size and worldwide cyber experience. Telefónica Tech maintains a worldwide network of service operations centers (SOCs) that spans the United Kingdom, Europe, and the Americas. These SOCs are responsible for supporting the 6,300 specialists and more than 4,000 certifications that it has in third-party technology. This consists of a Security Operations Center (SOC) located in Belfast, which offers crucial on-shore capabilities to Telefónica Tech UK&I by means of a facility that has been approved for security and is supported by worldwide resources., In order to anticipate and guard against new attacks, 'NextDefense' makes use of modern data sources, Big Data, and Artificial Intelligence (Machine Learning) methods. As a result, it is an essential component in the current cyber security scene. Through the implementation of this new service, Telefónica Tech UK&I is able to transform security operations by utilizing data and artificial intelligence, as well as by making extensive use of Security Orchestration, Automation, and Response (SOAR). This allows for the automation of cyber-attack prevention and response, the strengthening of security measures, the improvement of the overall security posture, the protection of customers from cyber threats, and the extraction of valuable information from the best available cyber intelligence.. Potential restraints include: Lack Of Data Security Awareness, Lack Of Security Expertise And Skilled Personnel. Notable trends are: Data security is in high demand in the manufacturing sector and is driving market growth.

The global Cloud and Internet of Things (IoT) Storage Technologies market is experiencing robust growth, driven by the increasing adoption of cloud computing, the proliferation of IoT devices, and the exponential growth of data generation across diverse sectors. This market, estimated at $500 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $1.5 trillion by 2033. Key drivers include the need for scalable and reliable data storage solutions, the demand for real-time data analytics from IoT devices, and the rising adoption of big data and AI applications. Significant market segments include cloud storage, IoT storage hardware, software, and services, with substantial application across manufacturing, banking, healthcare, and the burgeoning retail and transportation sectors. While market expansion is substantial, challenges remain, including data security concerns, interoperability issues among different IoT devices and platforms, and the need for robust data management strategies. The market's growth trajectory is further shaped by emerging trends such as edge computing, which processes data closer to the source, reducing latency and bandwidth demands; the increasing adoption of serverless architectures; and the development of advanced data analytics tools for extracting insights from diverse data streams. Geographical growth is diversified, with North America and Europe currently holding significant market share. However, the Asia-Pacific region is projected to show the highest growth rate over the forecast period driven by rapid technological advancements and digital transformation initiatives in countries like China and India. The competitive landscape is highly fragmented, featuring a mix of established technology giants and specialized storage providers, fostering innovation and competition within the market.

The size and share of the market is categorized based on Application (Data centers, Cloud computing, Enterprise IT infrastructure, AI and machine learning) and Product (Servers, Storage systems, Networking equipment, Data processing software, Cloud services) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The cloud analytics market, valued at $37.43 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 24.4% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of cloud computing across various industries provides a fertile ground for cloud-based analytics solutions. Businesses are increasingly seeking efficient ways to process and analyze large datasets, leading to higher demand for hosted data warehouse solutions and cloud BI tools. The need for real-time insights and improved decision-making capabilities fuels the growth of complex event processing solutions. Furthermore, the flexibility and scalability offered by public, hybrid, and private cloud deployment models cater to diverse organizational needs and budgets. Leading companies such as Microsoft, Amazon, and Salesforce are actively shaping the market landscape through continuous innovation and strategic acquisitions, further driving market expansion. The competitive landscape is characterized by both established players and emerging niche providers, fostering innovation and competition. However, certain challenges persist. Data security and privacy concerns, especially in regulated industries, remain a significant restraint. Integration complexities with existing on-premise systems and the need for skilled professionals to manage and interpret cloud analytics solutions pose hurdles for some organizations. Despite these challenges, the market is expected to maintain its high growth trajectory, fueled by the ongoing digital transformation across sectors and the increasing reliance on data-driven decision making. The geographical distribution of the market sees North America currently dominating due to early adoption and a robust technology ecosystem, however, APAC is poised for significant growth driven by increasing digitalization and investment in cloud infrastructure within countries like China and Japan. The continued development and refinement of cloud analytics solutions, combined with decreasing costs and improved accessibility, will propel further market expansion in the coming years.

Question Paper Solutions of Big Data Analysis (OEC-CS801A),8th Semester,Computer Science and Engineering,Maulana Abul Kalam Azad University of Technology