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.

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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, driven primarily by the surge in data generation across various industries. According to recent estimates, the global data volume is projected to reach 175 zettabytes by 2025, necessitating advanced data processing and analytical tools. Another key trend in the market is the increasing adoption of blockchain solutions to enhance big data implementation. This technology offers improved security, transparency, and immutability, making it an attractive option for businesses handling large volumes of sensitive data. However, the market also faces challenges, most notably the rise in data security issues. With the increasing adoption of cloud-based solutions and the growing use of Internet of Things (IoT) devices, the risk of data breaches and cyber-attacks is on the rise. Companies must invest in robust security measures to protect their data from unauthorized access and ensure compliance with data protection regulations. Additionally, the complexity of managing and analyzing large data sets can be a significant challenge, requiring specialized skills and resources. To capitalize on market opportunities and navigate these challenges effectively, businesses must stay abreast of the latest trends and technologies, and invest in training and development for their workforce.

What will be the Size of the Big Data Market during the forecast period?

Request Free SampleIn the ever-evolving world of big data, market dynamics continue to unfold, shaping the way businesses leverage data to drive innovation and gain competitive advantages. Artificial intelligence (AI) and data visualization tools are increasingly integrated into business processes, enabling real-time analytics and data-driven decision making. Financial analytics and data storytelling are essential components of data-driven innovation, providing insights into complex financial data and facilitating effective communication of data-driven insights. Data management tools and platforms are crucial for data integration, ensuring seamless data flow between various systems and applications. Data engineers and architects play a pivotal role in designing and implementing robust data infrastructure, while data governance professionals ensure data privacy and compliance. IoT analytics and machine learning are transforming industries, from healthcare to marketing, by providing actionable insights from vast amounts of data. Data monetization and data-driven business models are emerging trends, with companies exploring new revenue streams by leveraging their data assets. Data ethics and data literacy are becoming increasingly important, as businesses grapple with the ethical implications of data use and the need to equip employees with the skills to effectively analyze and interpret data. Predictive analytics and marketing analytics are also gaining traction, providing valuable insights into customer behavior and preferences. Data transformation is a continuous process, with new technologies and trends emerging regularly. Big data consulting and data engineering services are in high demand, as businesses seek to optimize their data strategies and stay ahead of the competition. Nosql databases, data lakes, and data mining are just a few of the many tools and techniques being used to manage and analyze large, complex data sets. In this dynamic landscape, data-driven decision making is the key to success. Companies that can effectively harness the power of their data, while ensuring data privacy and security, will be well-positioned to thrive in the digital age.

How is this Big Data Industry segmented?

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. DeploymentOn-premisesCloud-basedHybridTypeServicesSoftwareData TypeStructuredSemi-StructuredUnstructuredBusiness FunctionMarketing & SalesFinance & AccountingHuman ResourcesOperationsOthersVerticalsBanking, Financial Services, and Insurance (BFSI)Healthcare & Life SciencesRetail & Consumer GoodsIT & TelecomManufacturingGovernment & DefenseTransportation & LogisticsMedia & EntertainmentOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKMiddle East and AfricaEgyptKSAOmanUAEAPACChinaIndiaJapanSouth AmericaArgentinaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the realm of big data, on-premises and cloud-based deployment models continue to shape the market's dynamics. On-premises big data software solutions offer clients complete control over their hardware and sof

The share of organizations using big data analytics in market research worldwide steadily increased from 2014 to 2021, despite a slight drop in 2019. During the 2021 survey, 46 percent of respondents mentioned they used big data analytics as a research method.

The global big data analytics market size was valued at $307.52 billion in 2023 & is projected to grow from $348.21 billion in 2024 to $961.89 billion by 2032

The global Data Analytics Software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions, the expanding volume of big data, and the rising demand for data-driven decision-making across various industries. The market, valued at approximately $150 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% during the forecast period of 2025-2033. This significant expansion is fueled by several key factors. Businesses are increasingly recognizing the strategic importance of data analytics in optimizing operations, enhancing customer experiences, and gaining a competitive edge. The shift towards cloud-based solutions offers scalability, cost-effectiveness, and accessibility, making data analytics accessible to a broader range of businesses, from SMEs to large enterprises. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly into data analytics platforms, providing more sophisticated insights and predictive capabilities. The market's growth is further segmented by deployment model (on-premise vs. cloud-based) and user type (SMEs vs. large enterprises), reflecting the diverse needs and adoption rates across various business segments. While the market presents substantial opportunities, certain challenges persist. Data security and privacy concerns remain paramount, requiring robust security measures and compliance with evolving regulations. The complexity of implementing and managing data analytics solutions can also pose a barrier to entry for some organizations, requiring skilled professionals and substantial investments in infrastructure and training. Despite these challenges, the long-term outlook for the Data Analytics Software market remains highly positive, driven by continuous technological innovation, growing data volumes, and the increasing strategic importance of data-driven decision-making across industries. The market's evolution will continue to be shaped by the ongoing integration of AI and ML, the expansion of cloud-based offerings, and the increasing demand for advanced analytics capabilities. This dynamic landscape will present both challenges and opportunities for existing players and new entrants alike.

The Big Data Services market is experiencing robust growth, projected to reach $32.51 million in 2025 and maintain a significant Compound Annual Growth Rate (CAGR) of 27.81% from 2025 to 2033. This expansion is driven by several key factors. The increasing volume and complexity of data generated across various sectors, including IT and telecommunications, energy and power, BFSI (Banking, Financial Services, and Insurance), healthcare, retail, and manufacturing, necessitate sophisticated data management and analytical solutions. The shift towards cloud-based deployments offers scalability, cost-effectiveness, and enhanced accessibility, further fueling market growth. Moreover, advancements in artificial intelligence (AI), machine learning (ML), and big data analytics are creating new opportunities for businesses to extract valuable insights from their data, optimizing operations, improving decision-making, and enhancing customer experiences. Competitive landscape analysis reveals a mix of established players like SAS Institute, IBM, and Microsoft, alongside emerging technology providers, indicating a dynamic and innovative market. Regional analysis suggests North America currently holds a substantial market share, driven by early adoption of big data technologies and a strong presence of key technology companies. However, the Asia-Pacific region is expected to witness rapid growth in the coming years due to increasing digitalization, expanding internet penetration, and rising government investments in digital infrastructure. The market segmentation by deployment (on-premise vs. cloud) and end-user industry reflects the diverse application of big data services across various sectors, each presenting unique growth opportunities and challenges. Restraints to market growth include concerns regarding data security, privacy, and the need for skilled professionals to manage and interpret complex datasets. Nevertheless, the ongoing technological advancements and the increasing reliance on data-driven decision-making across various industries are expected to overcome these challenges and propel the Big Data Services market towards sustained and significant growth throughout the forecast period. Recent developments include: May 2023 : Microsoft has introduced Microsft fabric an softend-to-end, Unified Analytics Platform, which enables organisations to integrate all data and analytical tools they need, Where By making it possible for data and business professionals to unlock their potential, as well as lay the foundation for an era of Artificial Intelligence, fabric creates a single unified product that brings together technologies like Azure Data Factory, Azure Synapse Analytics, and Power BI., November 2022: Amazon Web Services, Inc. (AWS) released five new features in its database and analytics portfolios. These updates enable users to manage and analyze data at a petabyte scale more efficiently and quickly, simplifying the process for customers to operate the high-performance database and analytics workloads at scale., October 2022: Oracle introduced the Oracle Network Analytics Suite, which includes a new cloud-native portfolio of analytics tools. This suite enables operators to make more automated and informed decisions regarding the performance and stability of their entire 5G network core by combining network function data with machine learning and artificial intelligence.. Key drivers for this market are: Increasing Cloud Adoption And Rise In The Data Volume Generated, Increasing Demand For Improving Organization's Internal Efficiency; Growing Adoption of Private Cloud. Potential restraints include: Data Security Concerns. Notable trends are: Growing Adoption of Private Cloud is Driving the Market.

Big Data in Automotive Industry Insights and It is Segmented by Application (Product Development, Supply Chain and Manufacturing, OEM Warranty and Aftersales/Dealers, Connected Vehicle and Intelligent Transportation, and Sales, Marketing, and Other Applications) and Geography (North America, Europe, Asia-Pacific, and Rest of the World). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

This dataset contains more than 700,000 unique voltage vs. capacity curves. It was calculated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis" (Energies, under review) for more details.

This dataset was compiled with a resolution of 0.01 for the triplets and C/25 charges. This accounts for more than 5,000 different paths. Each path was simulated with at most 0.85% increases for each degradation mode.

4 Variables are included, see read me file for details and example how to use.

The global Online Analytical Processing (OLAP) tools market is experiencing robust growth, driven by the increasing demand for data-driven decision-making across various industries. The market, currently estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The widespread adoption of cloud-based OLAP solutions offers scalability, cost-effectiveness, and accessibility, attracting both large enterprises and SMEs. Furthermore, the rising volume and complexity of data necessitate sophisticated analytical tools for effective data mining and business intelligence. The emergence of advanced analytics capabilities, such as predictive modeling and machine learning integration within OLAP platforms, further enhances their value proposition. The expanding adoption of big data technologies and the growing need for real-time business insights are also contributing to the market's growth trajectory. However, the market faces some challenges. High implementation costs, especially for on-premises solutions, can hinder adoption, particularly among smaller businesses. The complexity of integrating OLAP tools with existing IT infrastructure can also pose a barrier. Additionally, the need for skilled professionals to effectively utilize and manage OLAP systems creates a talent gap that could impact market growth. Despite these constraints, the long-term outlook for the OLAP tools market remains positive, driven by ongoing technological advancements, increasing data volumes, and the persistent need for data-driven decision-making across sectors. The market's segmentation by deployment type (cloud-based vs. on-premises) and user type (large enterprises vs. SMEs) highlights diverse growth opportunities for vendors specializing in specific segments. This comprehensive report provides an in-depth analysis of the global Online Analytical Processing (OLAP) tools market, projecting a value of approximately $15 billion by 2025. We examine market concentration, key trends, dominant segments, product insights, and future growth catalysts. This report is crucial for businesses seeking to understand this rapidly evolving landscape and make informed strategic decisions. Keywords: OLAP, Online Analytical Processing, Business Intelligence, Data Analytics, Data Visualization, Cloud-Based BI, On-Premise BI, Big Data Analytics, Data Warehousing.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

The Big Data In Oil And Gas Market size was valued at USD 3.6 USD Billion in 2023 and is projected to reach USD 12.24 USD Billion by 2032, exhibiting a CAGR of 19.1 % during the forecast period. In the oil and gas industry, Big Data encompasses a huge and diverse set of information derived from many sources including oil and gas well seismic surveys, drilling, and production equipment. To put these datasets in context, it is useful to characterize them by the four V’s: volume, velocity, variety, and veracity. These include structured data from sources such as sensors and logs, semi-structured data from parcels such as emails and XML among others, and unstructured data from sources like social media platforms and technical reports among others. Some of the features include real-time mode of computations, predictive analysis, and data consolidation. Examples include: boosting the efficiency of exploration and production functions, improving equipment reliability through simulations and data analytics of equipment maintenance and performance patterns, ensuring safety measures, and better decision-making due to analytical capabilities. Key drivers for this market are: Growing Focus on Safety and Organization to Fuel Market Growth. Potential restraints include: Increasing Focus on Renewable Energy & Stringent Regulations for Protecting Environment to Hamper Growth. Notable trends are: Growth of IT Infrastructure to Bolster the Demand for Modern Cable Tray Management Solutions.

The advanced analytics market size is projected to be worth US$ 15,149.8 million in 2024. The market is likely to reach US$ 26,688.0 million by 2034. The market is further expected to surge at a CAGR of 5.8% during the forecast period 2024 to 2034.

Attributes	Key Insights
Advanced Analytics Market Estimated Size in 2024	US$ 15,149.8 million
Projected Market Value in 2034	US$ 26,688.0 million
Value-based CAGR from 2024 to 2034	5.8%

2019 to 2023 Historical Analysis vs. 2024 to 2034 Market Forecast Projections

Report Attributes	Details
Market Value in 2019	US$ 11,954.1 million
Market Value in 2023	US$ 14,355.5 million
CAGR from 2019 to 2023	5.5%

Country-wise Insights

The United States	2.3%
The United Kingdom	3.2%
India	7.6%
China	6.7%
Japan	8.8%

Category-wise Insights

Category	Shares in 2024
Big Data Analytics	23.3%
BFSI	22.6%

Report Scope

Attribute	Details
Estimated Market Size in 2024	US$ 15,149.8 million
Projected Market Valuation in 2034	US$ 26,688.0 million
Value-based CAGR 2024 to 2034	5.8%
Forecast Period	2024 to 2034
Historical Data Available for	2019 to 2023
Market Analysis	Value in US$ million
Key Regions Covered	North America Latin America Western Europe Eastern Europe South Asia and Pacific East Asia The Middle East & Africa
Key Market Segments Covered	Solution Industry Region
Key Countries Profiled	The United States Canada Brazil Mexico Germany France France Spain Italy Russia Poland Czech Republic Romania India Bangladesh Australia New Zealand China Japan South Korea GCC countries South Africa Israel
Key Companies Profiled	Altair Engineering, Inc. Fair Isaac Corporation International Business Machines Corporation KNIME Microsoft Corporation Oracle Corporation RapidMiner, Inc. SAP SE SAS Institute Inc. Trianz Infor Teradata

Systematic reviews are the method of choice to synthesize research evidence. To identify main topics (so-called hot spots) relevant to large corpora of original publications in need of a synthesis, one must address the “three Vs” of big data (volume, velocity, and variety), especially in loosely defined or fragmented disciplines. For this purpose, text mining and predictive modeling are very helpful. Thus, we applied these methods to a compilation of documents related to digitalization in aesthetic, arts, and cultural education, as a prototypical, loosely defined, fragmented discipline, and particularly to quantitative research within it (QRD-ACE). By broadly querying the abstract and citation database Scopus with terms indicative of QRD-ACE, we identified a corpus of N = 55,553 publications for the years 2013–2017. As the result of an iterative approach of text mining, priority screening, and predictive modeling, we identified n = 8,304 potentially relevant publications of which n = 1,666 were included after priority screening. Analysis of the subject distribution of the included publications revealed video games as a first hot spot of QRD-ACE. Topic modeling resulted in aesthetics and cultural activities on social media as a second hot spot, related to 4 of k = 8 identified topics. This way, we were able to identify current hot spots of QRD-ACE by screening less than 15% of the corpus. We discuss implications for harnessing text mining, predictive modeling, and priority screening in future research syntheses and avenues for future original research on QRD-ACE. Dataset for: Christ, A., Penthin, M., & Kröner, S. (2019). Big Data and Digital Aesthetic, Arts, and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 089443931988845. https://doi.org/10.1177/0894439319888455:

The Saudi Arabia Big Data and Artificial Intelligence Market Report is Segmented by Solutions (Hardware, Software, Service), Organization Size (SMEs, Large Enterprises), and End User (IT and Telecom, Retail, Public and Government Institutions, BFSI, Healthcare, Energy, Construction and Manufacturing, and Other End Users). The Market Size and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Data Ingestion Tool market is experiencing robust growth, driven by the exponential increase in data volume and velocity across industries. The market's expansion is fueled by the rising adoption of cloud-based solutions, offering scalability and cost-effectiveness compared to on-premises deployments. Large enterprises are significantly contributing to this growth, leveraging these tools for advanced analytics, real-time data processing, and improved decision-making. However, the market also faces challenges, including data security concerns and the complexity of integrating diverse data sources. The increasing demand for real-time data analytics and the need for efficient data pipelines are key drivers pushing market expansion. The shift towards cloud-native architectures and the emergence of serverless computing further accelerate adoption. The competitive landscape is dynamic, with established players like Talend and Amazon (via Kinesis) competing with newer entrants offering specialized functionalities. Open-source tools like Apache Kafka and Apache NiFi remain popular, particularly for organizations prioritizing cost optimization and customization. Segmentation by application (SMEs vs. Large Enterprises) reveals that while large enterprises are currently the primary consumers, the growing adoption of data analytics by SMEs presents a significant opportunity for future market growth. Geographic analysis indicates that North America and Europe currently hold the largest market share, but the Asia-Pacific region is poised for rapid expansion due to increasing digitalization and technological advancements. Looking forward, the market is expected to maintain a healthy growth trajectory, driven by continuous technological innovation and the ever-increasing reliance on data-driven decision making across all sectors. This will likely lead to increased competition, further innovation, and a broader range of solutions tailored to specific industry needs.

The Big Data Technology market, currently valued at $213.15 million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 9.91% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume of data generated across various sectors, coupled with the need for advanced analytics to derive actionable insights, is a primary catalyst. Furthermore, the rising adoption of cloud-based big data solutions offers scalability, cost-effectiveness, and enhanced accessibility, driving market growth. The growing demand for real-time data processing and analysis across industries like Telecom & IT, BFSI (Banking, Financial Services, and Insurance), and Healthcare & Pharmaceuticals is further bolstering market expansion. Companies are increasingly investing in big data technologies to improve operational efficiency, enhance customer experiences, and gain a competitive edge through data-driven decision-making. While data security concerns and the need for skilled professionals pose some challenges, the overall market outlook remains positive, driven by continuous technological advancements and increasing data generation across diverse sectors. The market segmentation reveals a significant contribution from the cloud delivery mode, reflecting the industry's shift towards flexible and scalable solutions. Among end-user verticals, Telecom & IT, BFSI, and Healthcare & Pharmaceuticals are expected to lead the growth, showcasing the high demand for data analytics across these sectors. Leading players like IBM, Microsoft, and Oracle are strategically positioning themselves to capitalize on this growth, investing in research and development and forming strategic partnerships to expand their market reach. Geographic analysis indicates strong growth in North America and Asia, driven by increased digitalization and technological advancements in these regions. The forecast period of 2025-2033 promises substantial growth opportunities, with the market expected to reach significant size by 2033, fueled by the continuous evolution of big data technologies and their widespread adoption across diverse industries and geographies. Big Data Technology Market Report: 2019-2033 This comprehensive report provides an in-depth analysis of the Big Data Technology industry, covering the period from 2019 to 2033. With a focus on the estimated year 2025 and a forecast period extending to 2033, this report offers invaluable insights for businesses seeking to navigate the complexities of this rapidly evolving market. The report analyzes key segments, including delivery modes (on-premise vs. cloud), end-user verticals (Telecom & IT, Energy & Power, BFSI, Retail, Manufacturing, Aerospace & Defense, Engineering & Construction, Healthcare & Pharmaceuticals, and Others), and the impact of recent industry developments on market growth and potential. The global market size is expected to reach hundreds of billions of dollars by 2033. Recent developments include: March 2023: Hewlett-Packard Company has announced a collaboration deal to acquire OpsRamp, an IT operations management (ITOM) company that observes, monitors, automates, and manages IT infrastructure, cloud resources, workloads, and applications for hybrid and multi-cloud surroundings, integrating OpsRamp’s hybrid digital operations management solution with the HPE GreenLake edge-to-cloud platform and helping it with HPE services will lower the operational complexity of multi-cloud IT environments that are in the public cloud, on-premises, and colocations., March 2023: Oracle has announced an extended collaboration with NVIDIA to include running strategic NVIDIA AI applications on the new Oracle cloud infrastructure Supercluster. NVIDIA has selected OCI as the first hyper-scale cloud provider to offer NVIDIA DGX Cloud, an AI supercomputing service, at a massive scale. In addition, NVIDIA is running NVIDIA AI Foundations, its new generative AI cloud services, which are available through DGX Cloud on OCI.. Key drivers for this market are: Increasing Adoption of Data Discovery and Visualization Tools is Expanding the Market Growth. Potential restraints include: Hacking and Tampering of Generated Data by Insiders or Third Party is Challenging the Market Growth. Notable trends are: Retail Industry to Dominate the Market.

The Structured Data Management Software market is experiencing robust growth, driven by the increasing need for efficient data handling and analysis across diverse industries. The market's expansion is fueled by several key factors, including the rising volume and complexity of data generated by businesses, the growing adoption of cloud-based solutions offering scalability and cost-effectiveness, and the increasing demand for advanced analytics capabilities to derive actionable insights. The shift towards digital transformation and the imperative to comply with data governance regulations further accelerates market growth. While the on-premises segment currently holds a significant share, cloud-based solutions are witnessing rapid adoption due to their flexibility and accessibility. Large enterprises are major consumers of these solutions, but SMEs are increasingly adopting them to streamline their operations and enhance decision-making. The competitive landscape is characterized by a mix of established players like Google, Salesforce, and IBM, alongside specialized vendors offering niche solutions. Geographic growth is widespread, with North America and Europe currently leading the market due to high technological adoption and robust digital infrastructure. However, Asia-Pacific is emerging as a key growth region, driven by rapid digitalization and increasing investments in technology infrastructure across countries like India and China. The market's future trajectory suggests continued expansion, driven by ongoing technological advancements, such as advancements in AI and machine learning integration within data management platforms. The projected Compound Annual Growth Rate (CAGR) for the Structured Data Management Software market suggests a steady increase in market value over the forecast period (2025-2033). This growth is expected to be influenced by the continuous development of innovative solutions catering to evolving business needs. While challenges such as data security concerns and the complexity of integrating different data sources may pose some restraints, the overall market outlook remains positive. The ongoing investments in research and development, along with the strategic partnerships and acquisitions among market players, are further enhancing the market's potential. The segmentation based on application (SMEs vs. Large Enterprises) and deployment (Cloud vs. On-premises) will continue to evolve, with cloud-based solutions increasingly dominating the market due to their inherent benefits. The regional breakdown highlights growth opportunities in emerging markets, demanding a focus on localized solutions and strategic partnerships to enhance penetration.

IntroductionIn recent years, numerous AI tools have been employed to equip learners with diverse technical skills such as coding, data analysis, and other competencies related to computational sciences. However, the desired outcomes have not been consistently achieved. This study aims to analyze the perspectives of students and professionals from non-computational fields on the use of generative AI tools, augmented with visualization support, to tackle data analytics projects. The focus is on promoting the development of coding skills and fostering a deep understanding of the solutions generated. Consequently, our research seeks to introduce innovative approaches for incorporating visualization and generative AI tools into educational practices.MethodsThis article examines how learners perform and their perspectives when using traditional tools vs. LLM-based tools to acquire data analytics skills. To explore this, we conducted a case study with a cohort of 59 participants among students and professionals without computational thinking skills. These participants developed a data analytics project in the context of a Data Analytics short session. Our case study focused on examining the participants' performance using traditional programming tools, ChatGPT, and LIDA with GPT as an advanced generative AI tool.ResultsThe results shown the transformative potential of approaches based on integrating advanced generative AI tools like GPT with specialized frameworks such as LIDA. The higher levels of participant preference indicate the superiority of these approaches over traditional development methods. Additionally, our findings suggest that the learning curves for the different approaches vary significantly. Since learners encountered technical difficulties in developing the project and interpreting the results. Our findings suggest that the integration of LIDA with GPT can significantly enhance the learning of advanced skills, especially those related to data analytics. We aim to establish this study as a foundation for the methodical adoption of generative AI tools in educational settings, paving the way for more effective and comprehensive training in these critical areas.DiscussionIt is important to highlight that when using general-purpose generative AI tools such as ChatGPT, users must be aware of the data analytics process and take responsibility for filtering out potential errors or incompleteness in the requirements of a data analytics project. These deficiencies can be mitigated by using more advanced tools specialized in supporting data analytics tasks, such as LIDA with GPT. However, users still need advanced programming knowledge to properly configure this connection via API. There is a significant opportunity for generative AI tools to improve their performance, providing accurate, complete, and convincing results for data analytics projects, thereby increasing user confidence in adopting these technologies. We hope this work underscores the opportunities and needs for integrating advanced LLMs into educational practices, particularly in developing computational thinking skills.

The dataset is comprised of text responses from GPT-4 after reading passages of text. It is scored and organized in folders.

The Wide Column Database (WCDB) software market is experiencing robust growth, driven by the increasing need for handling massive, unstructured datasets in real-time. The market, currently valued at approximately $5 billion in 2025, is projected to expand significantly over the next decade. This growth is fueled by several key factors, including the proliferation of big data applications across various sectors such as finance, healthcare, and e-commerce. Organizations are increasingly adopting cloud-based WCDB solutions to leverage scalability, cost-effectiveness, and enhanced agility. The preference for cloud-based solutions over on-premise deployments is a significant trend, driven by the reduced infrastructure management overhead and pay-as-you-go pricing models. While large enterprises are major adopters, the increasing adoption by SMEs signifies a broadening market reach. However, challenges like data security concerns, the complexity of managing WCDB systems, and the need for skilled personnel to operate and maintain them remain potential restraints on market growth. Competition is fierce, with established players like Microsoft, AWS, and Google alongside specialized WCDB vendors like ScyllaDB and DataStax vying for market share. The market's regional distribution shows strong presence in North America and Europe, with significant growth potential in the Asia-Pacific region driven by increasing digitalization and data generation in countries like India and China. The forecast period of 2025-2033 suggests a Compound Annual Growth Rate (CAGR) of approximately 15%, indicating substantial market expansion. This growth will likely be fueled by continued advancements in WCDB technology, improved integration with other data processing tools, and the growing adoption of advanced analytics techniques relying on large-scale data processing. The increasing adoption of NoSQL databases in general is a strong tailwind for WCDB solutions. Segmentation by application (large enterprises vs. SMEs) and deployment type (cloud vs. on-premise) provides valuable insights for market players in understanding distinct customer needs and tailoring their offerings accordingly. Focusing on ease of use and robust security features will be crucial for continued market penetration, particularly amongst SMEs. Strategic partnerships and acquisitions are expected to shape the competitive landscape further, potentially leading to market consolidation in the years to come.