Private businesses in the United States hired 37 thousand workers in May of 2025 compared to 60 thousand in April of 2025. This dataset provides the latest reported value for - United States ADP Employment Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Data Historian Market Outlook

The Data Historian market size is experiencing significant growth, driven by an increasing demand for efficient data management and analytics solutions across various industries. In 2023, the global Data Historian market was valued at approximately USD 1.2 billion and is anticipated to reach USD 2.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is attributed to a myriad of factors, including the accelerating adoption of industrial IoT, the need for real-time data analysis, and the increasing focus on process optimization and predictive maintenance.

One of the primary growth factors for the Data Historian market is the burgeoning Industrial Internet of Things (IIoT) landscape. As industries such as oil and gas, chemicals, and pharmaceuticals increasingly integrate smart devices and sensors into their operations, the volume of data generated is rising exponentially. Data historians play a crucial role in capturing, storing, and analyzing this data, providing organizations with actionable insights that drive efficiency and innovation. Moreover, the rise in big data analytics has further underscored the importance of effective data management tools, positioning data historians as indispensable components in the digital transformation journey of industrial players.

Another significant driver of the market is the increasing demand for real-time data analysis. In today's fast-paced industrial environment, the ability to monitor processes and equipment performance in real-time is critical. Data historians provide this capability, allowing organizations to identify and address potential issues promptly, thereby reducing downtime and improving overall operational efficiency. Furthermore, real-time data insights empower companies to make data-driven decisions that enhance productivity and competitiveness, a trend that is expected to continue fueling the market's expansion over the coming years.

The shift towards predictive maintenance is also propelling the Data Historian market forward. With the goal of minimizing unexpected equipment failures and optimizing maintenance schedules, industries are increasingly leveraging data historians to analyze historical data and predict future performance trends. This proactive approach not only reduces maintenance costs but also extends the lifespan of critical assets. As predictive maintenance becomes a standard practice across various sectors, the demand for robust data historian solutions is expected to surge, contributing to the market's sustained growth.

From a regional perspective, the Data Historian market is witnessing diverse growth patterns. North America currently holds a significant share of the market, driven by the presence of established industrial sectors and the early adoption of advanced technologies. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest growth rate over the forecast period. This can be attributed to the rapid industrialization in countries like China and India, coupled with increasing investments in infrastructure and technology. Europe is also expected to see steady growth, supported by stringent regulatory standards and a strong focus on sustainability and energy efficiency within its industrial landscape.

Component Analysis

The Data Historian market is segmented by components into software and services. The software segment constitutes the core of the market, representing the platforms and applications that facilitate data collection, storage, and analysis. As industrial processes become increasingly digitized, the demand for sophisticated software solutions that can handle vast amounts of data is on the rise. Data historian software is designed to efficiently capture and manage time-series data, providing users with the tools needed for detailed analysis and decision-making. The software's ability to integrate with various IT and OT systems further enhances its value, making it a critical asset for organizations looking to harness the power of data.

On the other hand, the services segment encompasses a range of offerings that support the deployment and optimization of data historian solutions. This includes implementation services, consulting, training, and support services. As businesses strive to maximize the value of their data historian investments, the demand for expert guidance and support is increasing. Service providers play a vital role in ensuring that data historian systems are effectively integrated into existing operations, tailored to meet the s

This dataset provides gridded modelled sub-daily and daily hydrological time series forced with meteorological observations. The data set is a consistent representation of the most important hydrological variables across the European Flood Awareness System (EFAS) domain. The temporal resolution is up to 30 years modelled time series of:

River discharge Volumetric soil moisture Snow water equivalent Soil wetness index (root zone) Runoff water equivalent (surface plus subsurface)

Also provided are auxiliary (time invariant) data to aid interpretation of river discharge and soil moisture data. These auxiliary data are the upstream area, elevation, soil depth, wilting capacity and field capacity. The latter three are provided at three soil levels, one for each of the three soil layers represented in LISFLOOD. This dataset was produced by forcing the open-source LISFLOOD hydrological model with gridded observational data of precipitation and temperature at a 1x1 arcminute resolution (~1.5 km at EFAS latitudes) across the EFAS domain. Previous versions of the data have a 5x5km resolution. For the latest version data is available from 1992-01-01 up until near-real time, with a delay of 6 days. The real-time data is only available to EFAS partners. Companion datasets, also available through the EWDS, are forecasts for users who are looking medium-range forecasts, reforecasts for research, local skill assessment and post-processing, and seasonal forecasts and reforecasts for users looking for long-term forecasts. For users looking for global hydrological data, we refer to the Global Flood Awareness System (GloFAS) forecasts and historical simulations. All these datasets are part of the operational flood forecasting within the Copernicus Emergency Management Service (CEMS), which is managed, technically implemented and developed by the European Commission’s Joint Research Centre.

CRB Index rose to 368.58 Index Points on June 6, 2025, up 0.77% from the previous day. Over the past month, CRB Index's price has risen 4.21%, and is up 8.71% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on June of 2025.

Transport Performance and Analytics (TPA) provides projections of workforce at the small area (Travel Zone or TZ) level for the Sydney Greater Metropolitan Area (GMA).

The GMA includes the Sydney Greater Capital City Statistical Area (GCCSA), the Southern Highlands and Shoalhaven SA4, Illawarra SA4, Newcastle and Lake Macquarie SA4, and Lower Hunter, Port Stephens, and Maitland SA3s, as defined by the Australian Bureau of Statistics (ABS). TPA workforce projections are five-yearly, from 2011 to 2056 and relate to usual residents of the GMA aged 15 years and over who are employed. They are estimates of employed people based on where they reside. TPA also produces employment projections based on the workplace or job location. They refer to persons aged 15 years and over, working in the GMA regardless of their place of usual residence. The majority of the persons employed in the GMA also reside in the GMA.

Factors considered in the estimation of workforce projections include: population by age and gender; participation rates; unemployment rates; historical labour force data; past trends of employment in each industry and the forecasts of industry growth or decline in each region.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How do you determine buy or sell? (LON:PXEN Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Data Entry Service Market Outlook

The global data entry service market size is poised to experience significant growth, with the market expected to rise from USD 2.5 billion in 2023 to USD 4.8 billion by 2032, achieving a Compound Annual Growth Rate (CAGR) of 7.5% over the forecast period. This growth can be attributed to several factors including the increasing adoption of digital technologies, the rising demand for data accuracy and integrity, and the need for businesses to manage vast amounts of data efficiently.

One of the key growth factors driving the data entry service market is the rapid digital transformation across various industries. As businesses continue to digitize their operations, the volume of data generated has increased exponentially. This data needs to be accurately entered, processed, and managed to derive meaningful insights. The demand for data entry services has surged as companies seek to outsource these non-core activities, enabling them to focus on their primary business operations. Additionally, the widespread adoption of cloud-based solutions and big data analytics has further fueled the demand for efficient data management services.

Another significant driver of market growth is the increasing need for data accuracy and integrity. Inaccurate or incomplete data can lead to poor decision-making, financial losses, and a decrease in operational efficiency. Organizations are increasingly recognizing the importance of maintaining high-quality data and are investing in data entry services to ensure that their databases are accurate, up-to-date, and reliable. This is particularly crucial for industries such as healthcare, BFSI, and retail, where precise data is essential for regulatory compliance, customer relationship management, and operational efficiency.

The cost-effectiveness of outsourcing data entry services is also contributing to market growth. By outsourcing these tasks to specialized service providers, organizations can save on labor costs, reduce operational expenses, and improve productivity. Service providers often have access to advanced tools and technologies, as well as skilled professionals who can perform data entry tasks more efficiently and accurately. This not only leads to cost savings but also allows businesses to reallocate resources to more strategic activities, driving overall growth.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the data entry service market during the forecast period. This can be attributed to the region's strong IT infrastructure, the presence of numerous outsourcing service providers, and the growing adoption of digital technologies across various industries. North America and Europe are also significant markets, driven by the high demand for data management services in sectors such as healthcare, BFSI, and retail. The Middle East & Africa and Latin America are anticipated to experience steady growth, supported by increasing investments in digital infrastructure and the rising awareness of the benefits of data entry services.

Service Type Analysis

The data entry service market can be segmented into various service types, including online data entry, offline data entry, data processing, data conversion, data cleansing, and others. Each of these service types plays a crucial role in ensuring the accuracy, integrity, and usability of data. Online data entry services involve entering data directly into an online system or database, which is essential for real-time data management and accessibility. This service type is particularly popular in industries such as e-commerce, where timely and accurate data entry is critical for inventory management and customer service.

Offline data entry services, on the other hand, involve entering data into offline systems or databases, which are later synchronized with online systems. This service type is often used in industries where internet connectivity may be unreliable or where data security is a primary concern. Offline data entry is also essential for processing historical data or data that is collected through physical forms and documents. The demand for offline data entry services is driven by the need for accurate and timely data entry in sectors such as manufacturing, government, and healthcare.

Data processing services involve the manipulation, transformation, and analysis of raw data to produce meaningful information. This includes tasks such as data validation, data sorting, data aggregation, and data analysis. Data processing is a critical componen

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Buy, Sell, or Hold? (ON Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

When should you buy or sell a stock? (LON:EXR Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

FRBOP Forecast: Annual Unemp Rate: Mean: sa: Current data was reported at 3.932 % in Jun 2018. This records a decrease from the previous number of 3.944 % for Mar 2018. FRBOP Forecast: Annual Unemp Rate: Mean: sa: Current data is updated quarterly, averaging 5.778 % from Sep 1981 (Median) to Jun 2018, with 148 observations. The data reached an all-time high of 10.281 % in Mar 1983 and a record low of 3.932 % in Jun 2018. FRBOP Forecast: Annual Unemp Rate: Mean: sa: Current data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.G022: Current Population Survey: Unemployment Rate: Seasonally Adjusted: Forecast: Federal Reserve Bank of Philadelphia.

FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data was reported at 2.226 % in Mar 2019. This records a decrease from the previous number of 2.327 % for Dec 2018. FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data is updated quarterly, averaging 1.951 % from Mar 2007 (Median) to Mar 2019, with 49 observations. The data reached an all-time high of 2.365 % in Jun 2018 and a record low of 1.127 % in Mar 2009. FRBOP Forecast: Core CPI Inflation: sa: Mean: Plus 1 Qtr data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.I008: Consumer Price Index: Urban: sa: Forecast: Federal Reserve Bank of Philadelphia.

FOMC Projection: Unemployment Rate: Central Tendency: Y3: Lower End data was reported at 4.000 % in Dec 2024. This stayed constant from the previous number of 4.000 % for Sep 2024. FOMC Projection: Unemployment Rate: Central Tendency: Y3: Lower End data is updated quarterly, averaging 3.850 % from Sep 2015 (Median) to Dec 2024, with 20 observations. The data reached an all-time high of 4.700 % in Sep 2015 and a record low of 3.200 % in Dec 2021. FOMC Projection: Unemployment Rate: Central Tendency: Y3: Lower End data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.G041: Current Population Survey: Unemployment Rate: Projection: Federal Reserve Board.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Should You Buy, Sell, or Hold? (SRE Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Envestnet®| Yodlee®'s Consumer Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

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Data Center Switch Market Size 2025-2029

The data center switch market size is forecast to increase by USD 4.7 billion at a CAGR of 5.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing need for simplified data center management and automation, as well as the surging demand for reliable Internet connectivity. The market, however, faces challenges that impact its growth potential. High operational costs associated with data center switches remain a significant hurdle, as organizations strive to balance the need for advanced networking capabilities with budget constraints. Additionally, regulatory hurdles and supply chain inconsistencies can temper growth, as organizations navigate complex compliance requirements and supply chain complexities.
To capitalize on market opportunities and navigate challenges effectively, companies must focus on innovative solutions that address these issues, such as energy-efficient switches, automated management systems, and flexible supply chain strategies. By staying abreast of market trends and addressing key challenges, organizations can optimize their data center infrastructure and drive business success. Network automation and fault tolerance are essential for maintaining network uptime and minimizing downtime.

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

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The market is experiencing significant evolution, driven by the increasing adoption of cloud-native networking and edge computing infrastructure. Network optimization and policy enforcement are key priorities for data center operations, as organizations seek to improve network resilience and ensure data center convergence. Data center design is shifting towards more efficient and scalable solutions, with a focus on network orchestration, virtualization, and segmentation. Network capacity management and virtual machine mobility are essential components of data center modernization, enabling businesses to effectively manage their infrastructure and respond to changing demands. Data center construction and migration are ongoing processes, with a growing emphasis on multi-cloud networking and disaster recovery strategies.
Network forensics and performance management are critical for maintaining compliance and ensuring business continuity. Data center cooling and airflow management are also crucial for maximizing efficiency and reducing costs. Network virtualization and data center standards are essential for ensuring interoperability and simplifying management. Network capacity management and traffic analysis are essential for optimizing network performance and ensuring network resilience. Data center compliance and automation are key areas of investment, as organizations seek to streamline operations and reduce manual processes. Overall, the market is dynamic and evolving, with a focus on innovation and efficiency.

How is this Data Center Switch Industry segmented?

The data center switch 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

  Core switches
  Distribution switches
  Access switches


Technology

  Ethernet
  Fiber channel
  Infiniband


End-user

  Cloud
  Telecom
  Enterprise
  Government


Speed

  10 G
  25 G
  40 G
  100 G
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The core switches segment is estimated to witness significant growth during the forecast period.

In the realm of data center networking, core switches serve a pivotal function in interconnecting distribution switches and delivering high-speed, high-capacity data traffic transmission within the data center. Two primary types of core switches exist in this context: chassis-based core switches and fixed-configuration core switches. Chassis-based core switches, comprised of a chassis or mainframe and interchangeable line cards or modules, offer customization based on the data center's unique networking demands. Conversely, fixed-configuration core switches present a set number of ports and features in a non-modular design. Data center security is a paramount concern, driving the adoption of advanced networking solutions.

Edge computing and hyper-converged infrastructure are transforming data center architecture, necessitating network configuration adaptability. Power consumption and thermal management remain critical challenges, fueling the demand for energy-efficient networking technologies. Network programmability and automation are essential for optimizing data center performance and reducing op

Access monthly energy price assessments for Germany, featuring Ethanol and other key energy commodities. Coverage includes 10-year price history, current values, short-term forecasts, and market trends. Updated on the 3rd business day of each month, the data offers insights on prices, supply, demand, production, and trade. Available via PDF reports, Excel Add-In, Power BI, and API. Coverage for United States and over 30 other countries is included in Intratec Energy Prices & Markets. Free preview available.

SDR holdings in United States was reported at 166889725043 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - SDR holdings - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

TagX is your trusted partner for stock market and financial data solutions. We specialize in delivering real-time and end-of-day data feeds that power software, trading algorithms, and risk management systems globally. Whether you're a financial institution, hedge fund, or individual investor, our reliable datasets provide essential insights into market trends, historical pricing, and key financial metrics.

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Access monthly energy price assessments for Germany, featuring Naphtha and other key energy commodities. Coverage includes 10-year price history, current values, short-term forecasts, and market trends. Updated on the 3rd business day of each month, the data offers insights on prices, supply, demand, production, and trade. Available via PDF reports, Excel Add-In, Power BI, and API. Coverage for Germany and over 30 other countries is included in Intratec Energy Prices & Markets. Free preview available.

Access monthly energy price assessments for Germany, featuring Kerosene and other key energy commodities. Coverage includes 10-year price history, current values, short-term forecasts, and market trends. Updated on the 3rd business day of each month, the data offers insights on prices, supply, demand, production, and trade. Available via PDF reports, Excel Add-In, Power BI, and API. Coverage for Spain and over 30 other countries is included in Intratec Energy Prices & Markets. Free preview available.