Massive Llc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The Big Data Healthcare Market Report is Segmented by Component (Software, Services), Deployment (On-Premise, Cloud), Analytics Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Application (Financial Analytics, and More), End User (Healthcare Providers, and More), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

List of the proteomics datasets used in this study.

Analysts from the U.S. Geological Survey and Lawrence Berkeley National Laboratory collaborated to develop and release the United States Large Scale Solar Photovoltaic Database (USPVDB). This effort built from the expertise gained while developing the regularly updated United States Wind Turbine Database (USWTDB). Starting from Energy Information Administration (EIA) data, locations of large-scale solar photovoltaic (LSPV) facilities were visually verified using high-resolution aerial imagery; a polygon was drawn around the extent of facility panel arrays, and facility attributes were appended. Quality assurance and control were achieved via team peer review and comparing the USPVDB to other datasets of U.S. solar photovoltaic facilities. Some facility information did not exist within our source data or not yet built, not built at all, or located elsewhere. Thus, uncertainty may exist for certain facilities and a confidence level of 1 to 4 is given for each. None of the data are field verified.

This statistic shows the leading vendors of big data and analytics software from 2015 to 2017. In 2017, Splunk was the largest big data and analytics software provider with ** percent of the market.

The Hadoop Big Data Analytics Market Report is Segmented by Solution (Data Discovery and Visualization (DDV), Advanced Analytics (AA), and More), End-Use Industry (BFSI, Retail, IT and Telecom, Healthcare and Life Sciences, and More), Deployment Mode (On-Premise, Cloud, and More), Organization Size (Large Enterprises and Small and Medium Enterprises), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

The Supply Chain Big Data Analytics Market Report is Segmented by Component (Solution, Service), End User Industry (Retail, Transportation and Logistics, Manufacturing, Healthcare, Other End-User Industries), Deployment Model (On-Premise, Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

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

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The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

Less than half of all MS/MS spectra acquired in shotgun proteomics typically result in a confident peptide match. Here we present an ultra-tolerant Sequest database search that allowed peptide matching even with modifications of unknown masses up to ±500 Da. From an HEK293 cell proteome-wide dataset (9,513 proteins and 396,736 peptides), a ±500-Da search matched an additional 184,000 modified peptides. These were linked to both biological and chemical modifications representing 523 distinct mass bins including phosphorylation, glycosylation, and methylation. We attempted to localize all unknown modification masses to specific regions within a peptide, and known modifications were accurately assigned to the correct amino acids with frequencies often >90%. These data demonstrate that a large fraction of previously unassignable spectra are assignable to peptide sequences with modifications.

Dataset

This dataset was created by Glitch_in_Vector

Contents

Chunk_0 for me, Choose others as you want.

The Big Data Analytics Market size is expected to reach USD 924.39 billion in 2023 growing at a CAGR of 10.2. In-depth segmentation with Big Data Analytics Market share, opportunities, trend analysis, and forecast to 2023.

This document, Innovating the Data Ecosystem: An Update of The Federal Big Data Research and Development Strategic Plan, updates the 2016 Federal Big Data Research and Development Strategic Plan. This plan updates the vision and strategies on the research and development needs for big data laid out in the 2016 Strategic Plan through the six strategies areas (enhance the reusability and integrity of data; enable innovative, user-driven data science; develop and enhance the robustness of the federated ecosystem; prioritize privacy, ethics, and security; develop necessary expertise and diverse talent; and enhance U.S. leadership in the international context) to enhance data value and reusability and responsiveness to federal policies on data sharing and management.

Database Management System Market Outlook

According to our latest research, the global Database Management System (DBMS) market size reached USD 85.5 billion in 2024, reflecting the sector’s robust expansion across various industries. The market is expected to grow at a CAGR of 11.8% from 2025 to 2033, culminating in a forecasted market size of USD 231.7 billion by 2033. This impressive growth is primarily driven by the escalating volume of data generated by digital transformation initiatives, rising adoption of cloud-based solutions, and the increasing complexity of enterprise data ecosystems.

One of the key growth factors for the Database Management System market is the proliferation of big data analytics and the need for real-time data processing. Organizations across sectors such as BFSI, healthcare, retail, and manufacturing are leveraging advanced DBMS solutions to derive actionable insights from massive datasets. The integration of artificial intelligence and machine learning into database management systems is further enhancing their analytical capabilities, enabling predictive analytics, automated data governance, and anomaly detection. As businesses continue to digitize their operations, the demand for scalable, secure, and high-performance DBMS platforms is expected to surge, fueling market expansion.

Another significant driver is the widespread migration to cloud-based database architectures. Enterprises are increasingly opting for cloud deployment due to its flexibility, cost-effectiveness, and ease of scalability. Cloud-based DBMS solutions allow organizations to manage data across multiple geographies with minimal infrastructure investment, supporting global expansion and remote work trends. The growth of hybrid and multi-cloud environments is also propelling the need for database management systems that can seamlessly integrate and synchronize data across diverse platforms. This shift is compelling vendors to innovate and offer more robust, cloud-native DBMS offerings.

The evolution of database types, particularly the rise of NoSQL and in-memory databases, is transforming the DBMS market landscape. Traditional relational databases are now complemented by NoSQL databases that cater to unstructured and semi-structured data, supporting use cases in IoT, social media, and real-time analytics. In-memory databases, known for their low latency and high throughput, are gaining traction in applications requiring instantaneous data access. This diversification of database technologies is enabling organizations to choose best-fit solutions for their specific needs, contributing to the overall growth and dynamism of the market.

From a regional perspective, North America dominates the Database Management System market due to its advanced IT infrastructure, high cloud adoption rates, and strong presence of major technology providers. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization in emerging economies, increasing investments in IT modernization, and the expansion of e-commerce and fintech sectors. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by regulatory compliance initiatives and the modernization of legacy systems. The global nature of data-driven business models ensures that demand for sophisticated DBMS solutions remains strong across all regions.

Component Analysis

The Database Management System market by component is segmented into software and services, each playing a pivotal role in the overall ecosystem. The software segment encompasses various types of DBMS platforms, including relational, NoSQL, and in-memory databases, which form the backbone of enterprise data management strategies. This segment holds the largest market share, driven by continuous innovations in database architectures, enhanced security features, and integration capabilities with emerging technologies such as AI and IoT. Organizations are increasingly investing in advanced DBMS software to manage the growing complexity and volume of data, ensure data integrity, and support mission-critical applications.

On the other hand, the services segment, which includes consulting, implementation, support, and maintenance, is experiencing rapid growth as enterprises seek to optimize their database environments. The complexity of modern database systems necessitates expert

The FLOTROP dataset contains numerous plant observations (around 340,000 occurrences) in northern tropical Africa (from the 5 th to 25th parallel north) in open ecosystems (savannah and steppe). They were collected by multiple collectors between 1920 and 2012 and were managed by Philippe Daget. These observations are probably the most important and unique source of plant distribution over the Sahel area. The data are now available in the Global Biodiversity Information Facility (GBIF), Tela Botanica website, and as maps in the African Plant Database. For the overall area involved, this dataset has increased by 40% the data available in the GBIF. For some countries between the 15th and 21st parallel north, the FLOTROP dataset has increased available occurrences 10-fold compared to the data existing in the GBIF.

Tropical northern Africa (herein defined as between the 5th and 25th parallel north) is mostly occupied by open ecosystems, such as steppe and savannah. The vegetation in these ecosystems is consumed by animals, either wildlife or livestock, and is also used by the local communities for food, energy or medicinal purposes. The open ecosystems in tropical northern Africa are of great importance to the economy, food security and human well-being. Plant diversity within these ecosystems is driven by many factors, such as the climate, soil, fire and grazing. Plant diversity in these regions is being greatly impacted by global change. Historical data are needed to understand species and diversity dynamics. The database presented in this work is the collection of numerous datasets gathered over the years. At the outset, the FLOTROP database was intended to store all the data recorded by IEMVT (French institute for tropical livestock production and veterinary medicine, now part of CIRAD) in the sixties. In 1993, CIRAD and CNRS set up a project to collect a maximum of botanical surveys within these regions. Two software packages were created by the team to manage the database. The first was created under DOS then a second was started under Windows using the APL DYALOG language. Data were collected and scanned between 1993 and 2016. We extracted the data from the software version. We shared the species occurrences recorded in the database on the Tela Botanica website (http://www.tela-botanica.org/) and on the GBIF database.

With the rapid development of data acquisition and storage space, massive datasets exhibited with large sample size emerge increasingly and make more advanced statistical tools urgently need. To accommodate such big volume in the analysis, a variety of methods have been proposed in the circumstances of complete or right censored survival data. However, existing development of big data methodology has not attended to interval-censored outcomes, which are ubiquitous in cross-sectional or periodical follow-up studies. In this work, we propose an easily implemented divide-and-combine approach for analyzing massive interval-censored survival data under the additive hazards model. We establish the asymptotic properties of the proposed estimator, including the consistency and asymptotic normality. In addition, the divide-and-combine estimator is shown to be asymptotically equivalent to the full-data-based estimator obtained from analyzing all data together. Simulation studies suggest that, relative to the full-data-based approach, the proposed divide-and-combine approach has desirable advantage in terms of computation time, making it more applicable to large-scale data analysis. An application to a set of interval-censored data also demonstrates the practical utility of the proposed method.

Discover Market Research Intellect's Big Data Pharmaceutical Advertising Market Report, worth USD 4.5 billion in 2024 and projected to hit USD 12.3 billion by 2033, registering a CAGR of 15.2% between 2026 and 2033.Gain in-depth knowledge of emerging trends, growth drivers, and leading companies.

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Data base of large business groups and their structure. Updated: Ad hoc. Data available for 2005/06

A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.

This dataset is designed for skill gap analysis, focusing on evaluating the skill gap between students’ current skills and industry requirements. It provides insights into technical skills, soft skills, career interests, and challenges, helping in skill gap analysis to identify areas for improvement.

By leveraging this dataset, educators, recruiters, and researchers can conduct skill gap analysis to assess students’ job readiness and tailor training programs accordingly. It serves as a valuable resource for identifying skill deficiencies and skill gaps improving career guidance, and enhancing curriculum design through targeted skill gap analysis.

Following is the column descriptors: Name - Student's full name. email_id - Student's email address. Year - The academic year the student is currently in (e.g., 1st Year, 2nd Year, etc.). Current Course - The course the student is currently pursuing (e.g., B.Tech CSE, MBA, etc.). Technical Skills - List of technical skills possessed by the student (e.g., Python, Data Analysis, Cloud Computing). Programming Languages - Programming languages known by the student (e.g., Python, Java, C++). Rating - Self-assessed rating of technical skills on a scale of 1 to 5. Soft Skills - List of soft skills (e.g., Communication, Leadership, Teamwork). Rating - Self-assessed rating of soft skills on a scale of 1 to 5. Projects - Indicates whether the student has worked on any projects (Yes/No). Career Interest - The student's preferred career path (e.g., Data Scientist, Software Engineer). Challenges - Challenges faced while applying for jobs/internships (e.g., Lack of experience, Resume building issues).