The Big Data Processing and Distribution Software market is booming, projected to reach $150 billion by 2033 with a 15% CAGR. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector. Discover regional market shares and growth opportunities in cloud-based solutions and enterprise deployments.

This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P) environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, target tracking, and density estimation to name a few, necessary for many emerging P2P applications in bioinformatics, webmining and sensor networks. Centralizing all or some of the data to build global models is impractical in such P2P environments because of the large number of data sources, the asynchronous nature of the P2P networks, and dynamic nature of the data/network. The proposed algorithm takes a two-step approach. In the monitoring phase, the algorithm checks if the model ‘quality’ is acceptable by using an efficient local algorithm. This is then used as a feedback loop to sample data from the network and rebuild the GMM when it is outdated. We present thorough experimental results to verify our theoretical claims.

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 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).

Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires significant insights into expected job runtimes and scaling behavior, resource characteristics, input data distributions, and other factors. Unable to estimate performance accurately, users frequently overprovision resources for their jobs, leading to low resource utilization and high costs. In this paper, we present major building blocks towards a collaborative approach for optimization of data processing cluster configurations based on runtime data and performance models. We believe that runtime data can be shared and used for performance models across different execution contexts, significantly reducing the reliance on the recurrence of individual processing jobs or, else, dedicated job profiling. For this, we describe how the similarity of processing jobs and cluster infrastructures can be employed to combine suitable data points from local and global job executions into accurate performance models. Furthermore, we outline approaches to performance prediction via more context-aware and reusable models. Finally, we lay out how metrics from previous executions can be combined with runtime monitoring to effectively re-configure models and clusters dynamically.

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 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).

About this dataset

Context

The dataset tabulates the Excel population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 45 to 49 years years with a population of 74 (15.64%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.42%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Age. You can refer the same here

DC Power Distribution for Data Centers Market Outlook

According to our latest research, the DC Power Distribution for Data Centers market size reached USD 6.3 billion globally in 2024, demonstrating robust growth driven by the increasing demand for energy-efficient and reliable power solutions in data center operations. The market is projected to expand at a CAGR of 7.8% from 2025 to 2033, with the forecasted market size expected to reach USD 12.4 billion by 2033. This growth is primarily attributed to the surging adoption of cloud computing, rapid digital transformation across industries, and the growing need for sustainable energy solutions within hyperscale and edge data centers worldwide.

The primary growth factor for the DC Power Distribution for Data Centers market is the escalating demand for high-efficiency power infrastructure that reduces energy losses and operational costs. Traditional AC power distribution systems in data centers are increasingly being replaced by direct current (DC) solutions, as DC systems offer significant energy savings, improved reliability, and simplified power conversion processes. The proliferation of high-density computing environments, especially with the rise of artificial intelligence, machine learning, and big data analytics, necessitates robust and scalable power distribution architectures that can handle variable loads efficiently. As organizations strive to minimize their carbon footprint and adhere to environmental regulations, DC power distribution emerges as a compelling choice for data centers seeking to optimize their energy usage and sustainability metrics.

Another notable driver for the market is the rapid expansion of hyperscale and colocation data centers, particularly in emerging economies. These large-scale facilities require advanced power distribution systems capable of supporting massive computational workloads and ensuring uninterrupted operations. The rise of edge computing, driven by the Internet of Things (IoT) and 5G deployment, is further amplifying the need for decentralized and scalable power solutions. DC power distribution systems are inherently more adaptable to modular and distributed data center architectures, making them well-suited for both hyperscale and edge environments. Additionally, the integration of renewable energy sources, such as solar and wind, into data center power infrastructure is facilitated by DC systems, which can directly utilize the output from these sources without the need for complex conversions.

Technological advancements and innovation in DC power components, such as intelligent power distribution units (PDUs), busways, and circuit breakers, are also fueling market growth. Manufacturers are focusing on developing smart, IoT-enabled DC power distribution solutions that offer real-time monitoring, predictive maintenance, and enhanced operational visibility. These innovations not only improve data center uptime and reliability but also enable operators to optimize power usage effectiveness (PUE) and reduce total cost of ownership (TCO). As data center operators prioritize digital transformation and automation, the demand for intelligent DC power distribution solutions is expected to witness substantial growth throughout the forecast period.

From a regional perspective, Asia Pacific is emerging as a dominant force in the global DC Power Distribution for Data Centers market, accounting for the largest share in 2024. This growth is underpinned by massive investments in digital infrastructure, the proliferation of cloud service providers, and government initiatives aimed at promoting energy efficiency across IT and telecom sectors. North America remains a mature and technologically advanced market, driven by the presence of major hyperscale data center operators and ongoing modernization of legacy infrastructure. Meanwhile, Europe is witnessing steady growth, fueled by stringent energy regulations and the increasing adoption of green data center practices. Latin America and the Middle East & Africa are also experiencing rising demand, albeit from a smaller base, as digital transformation initiatives gain momentum in these regions.

In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data is located at a central location. However, it becomes extremely challenging to perform the same when the data is distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world datasets in order to performance of the proposed algorithm.

The Big Data Analytics in Banking Market is Segmented by Type of Solutions (Data Discovery and Visualization (DDV) and Advanced Analytics (AA)), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

Market Data Distribution Platforms Market Outlook

According to our latest research, the market size of the global Market Data Distribution Platforms Market reached USD 8.7 billion in 2024, with a robust growth trajectory supported by a CAGR of 9.1% projected for the period 2025 to 2033. By the end of 2033, the market is expected to attain a value of USD 19.1 billion. This remarkable growth is primarily driven by the increasing demand for real-time data analytics and the rising adoption of cloud-based distribution solutions across financial institutions, telecommunications, and other data-intensive sectors. As per our latest research, the proliferation of algorithmic trading, regulatory mandates for transparency, and digital transformation initiatives are further propelling the adoption of advanced market data distribution platforms globally.

One of the most significant growth factors for the Market Data Distribution Platforms Market is the exponential rise in data volumes generated by financial markets and other industries. The surge in electronic trading, high-frequency trading, and the adoption of algorithmic strategies have necessitated the need for platforms that can distribute large volumes of market data with minimal latency and maximum reliability. Financial institutions, in particular, require real-time access to market data to make informed trading decisions and to comply with stringent regulatory requirements. The increasing complexity of financial instruments and the globalization of trading activities have made efficient data distribution a critical component of the financial services infrastructure. Furthermore, the growing integration of alternative data sources, such as social media sentiment and geospatial data, is pushing market data distribution platforms to evolve, ensuring they can handle diverse data types while maintaining speed and accuracy.

Another key driver is the widespread adoption of cloud technology and the shift towards hybrid IT environments. Organizations across sectors are recognizing the benefits of cloud-based market data distribution platforms, including scalability, flexibility, and cost efficiency. Cloud deployment allows enterprises to manage and distribute data seamlessly across geographically dispersed teams and trading desks, supporting business continuity and operational agility. Additionally, cloud platforms offer enhanced security features, disaster recovery capabilities, and the ability to integrate with advanced analytics and artificial intelligence tools. These advantages are particularly appealing to small and medium enterprises (SMEs), which may lack the resources to maintain extensive on-premises infrastructure but still require robust market data solutions to remain competitive.

The increasing regulatory scrutiny and the need for transparency in financial transactions are also fueling the demand for advanced market data distribution platforms. Regulatory bodies worldwide are enforcing rules that mandate accurate and timely dissemination of market data to ensure fair trading practices and to protect investors. Market participants must adhere to regulations such as MiFID II in Europe and the Dodd-Frank Act in the United States, which impose strict requirements on data reporting, order execution, and market surveillance. Compliance with these regulations necessitates the deployment of sophisticated data distribution systems capable of supporting real-time monitoring, audit trails, and secure data sharing. This regulatory landscape is compelling financial institutions and other end-users to upgrade their existing platforms or invest in new solutions that offer enhanced compliance features and reporting capabilities.

From a regional perspective, North America continues to hold the largest share of the Market Data Distribution Platforms Market, driven by the presence of major financial hubs, advanced IT infrastructure, and early adoption of innovative technologies. The United States, in particular, is home to leading financial institutions, trading firms, and exchanges that rely heavily on real-time data distribution solutions. Europe follows closely, with significant demand stemming from regulatory reforms and the expansion of electronic trading. The Asia Pacific region is emerging as a high-growth market, fueled by the rapid digitalization of financial services, increasing investments in fintech, and the proliferation of stock exchanges in countries such as China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by o

In 2019, around ** percent of the big data talent demand in China came from Beijing. Most of the IT-related talent demands were from Beijing, the hub of innovation in China.

The New York Data Center Market Report is Segmented by Data Center Size (Small, Medium, Large, Mega), Tier Standard (Tier I-II, Tier III, Tier IV), and Absorption (Utilized, Non-Utilized). The Market Forecasts are Provided in Terms of Value (MW).

About this dataset

Context

The dataset tabulates the Big Lake population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Lake. The dataset can be utilized to understand the population distribution of Big Lake by age. For example, using this dataset, we can identify the largest age group in Big Lake.

Key observations

The largest age group in Big Lake, TX was for the group of age Under 5 years years with a population of 346 (11.19%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Big Lake, TX was the 75 to 79 years years with a population of 17 (0.55%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Big Lake is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Big Lake total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Lake Population by Age. You can refer the same here

Big Sandy Distribution Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The distributed computing market is experiencing robust growth, driven by the increasing demand for high-performance computing, scalability, and resilience in various sectors. The market's expansion is fueled by the adoption of cloud computing, big data analytics, and artificial intelligence (AI), which necessitate decentralized processing capabilities. Businesses across diverse industries, including BFSI (Banking, Financial Services, and Insurance), manufacturing, healthcare, and energy, are leveraging distributed computing to manage large datasets, enhance operational efficiency, and improve decision-making. The market segmentation reveals a significant contribution from the software and services segment, indicating a growing preference for cloud-based solutions and managed services. North America and Europe currently hold the largest market share, driven by early adoption and well-established IT infrastructure. However, Asia-Pacific is anticipated to witness the fastest growth rate due to increasing digitalization and government initiatives promoting technological advancements. While the market faces challenges such as data security concerns and integration complexities, ongoing technological innovations and the growing need for real-time data processing are expected to overcome these limitations, fueling continued market expansion. The forecast period of 2025-2033 projects a substantial increase in market value, with a compound annual growth rate (CAGR) that reflects the accelerating adoption of distributed computing technologies. Specific application segments such as healthcare and life sciences are expected to demonstrate significant growth due to the increasing volume of genomic data and the need for advanced analytics. Key players like IBM, Intel, HPE, Google, and Microsoft are actively investing in research and development to enhance their offerings and maintain a competitive edge. The expansion of 5G networks and the proliferation of edge computing are anticipated to further contribute to the market's growth by enabling faster data processing and reduced latency. Future growth will depend on addressing security concerns, standardizing protocols, and simplifying the deployment and management of distributed computing systems.

The share of business enterprises which performed big data analysis in Ukraine amounted to approximately 13 percent in 2020, marking the highest figure over the period observed. In 2019, the share of enterprises processing big data decreased to less than ** percent.

The performance of eight different methods to predict human volume of distribution (VDss) using a large data set (N > 100) was evaluated.The accuracy was assessed by the end points % within two-fold and absolute average fold error (AAFE). The ability to rank order was accessed by the σ and bias was examined using average fold error. Significance of observed differences was established using statistical permutation testing.The Rodgers-Lukova equation, a tissue composition model, for acids and single species scaling based on rat for other ion classes showed the best results in absence of non-rodent data.The semimechanistic Øie-Tozer model based on all thee preclinical species showed the best performance overall (81% within two-fold, AAFE 1.55, σ 0.62). This was not statistically significantly better at the 95% confidence level than the same model based on two preclinical species or single species scaling from monkey. Thus, the use of primates appears difficult to justify when the sole goal is to extrapolate human volume of distribution. The performance of eight different methods to predict human volume of distribution (VDss) using a large data set (N > 100) was evaluated. The accuracy was assessed by the end points % within two-fold and absolute average fold error (AAFE). The ability to rank order was accessed by the σ and bias was examined using average fold error. Significance of observed differences was established using statistical permutation testing. The Rodgers-Lukova equation, a tissue composition model, for acids and single species scaling based on rat for other ion classes showed the best results in absence of non-rodent data. The semimechanistic Øie-Tozer model based on all thee preclinical species showed the best performance overall (81% within two-fold, AAFE 1.55, σ 0.62). This was not statistically significantly better at the 95% confidence level than the same model based on two preclinical species or single species scaling from monkey. Thus, the use of primates appears difficult to justify when the sole goal is to extrapolate human volume of distribution.

According to Cognitive Market Research, the global Data Quality Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS of

Data Quality Software

The Emergence of Big Data and IoT drives the Market

The rise of big data analytics and Internet of Things (IoT) applications has significantly increased the volume and complexity of data that businesses need to manage. As more connected devices generate real-time data, the amount of information businesses handle grows exponentially. This surge in data requires organizations to ensure its accuracy, consistency, and relevance to prevent decision-making errors. For instance, in industries like healthcare, where real-time data from medical devices and patient monitoring systems is used for diagnostics and treatment decisions, inaccurate data can lead to critical errors. To address these challenges, organizations are increasingly investing in data quality software to manage large volumes of data from various sources. Companies like GE Healthcare use data quality software to ensure the integrity of data from connected medical devices, allowing for more accurate patient care and operational efficiency. The demand for these tools continues to rise as businesses realize the importance of maintaining clean, consistent, and reliable data for effective big data analytics and IoT applications. With the growing adoption of digital transformation strategies and the integration of advanced technologies, organizations are generating vast amounts of structured and unstructured data across various sectors. For instance, in the retail sector, companies are collecting data from customer interactions, online transactions, and social media channels. If not properly managed, this data can lead to inaccuracies, inconsistencies, and unreliable insights that can adversely affect decision-making. The proliferation of data highlights the need for robust data quality solutions to profile, cleanse, and validate data, ensuring its integrity and usability. Companies like Walmart and Amazon rely heavily on data quality software to manage vast datasets for personalized marketing, inventory management, and customer satisfaction. Without proper data management, these businesses risk making decisions based on faulty data, potentially leading to lost revenue or customer dissatisfaction. The increasing volumes of data and the need to ensure high-quality, reliable data across organizations are significant drivers behind the rising demand for data quality software, as it enables companies to stay competitive and make informed decisions.

Key Restraints to

Data Quality Software

Lack of Skilled Personnel and High Implementation Costs Hinders the market growth

The effective use of data quality software requires expertise in areas like data profiling, cleansing, standardization, and validation, as well as a deep understanding of the specific business needs and regulatory requirements. Unfortunately, many organizations struggle to find personnel with the right skill set, which limits their ability to implement and maximize the potential of these tools. For instance, in industries like finance or healthcare, where data quality is crucial for compliance and decision-making, the lack of skilled personnel can lead to inefficiencies in managing data and missed opportunities for improvement. In turn, organizations may fail to extract the full value from their data quality investments, resulting in poor data outcomes and suboptimal decision-ma...