Explore the Top 50 US Tech Companies Dataset with metrics like revenue, market cap, employee size, and more. Perfect for market research, business analysis, and AI projects.

Mapped In list of cool Tech Companies NYC http://driverrestore.com/

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

The percentage of market value attributable to intangible assets has increased exponentially from 32% in 1985 to 87% in 2015. This trend is expected to continue, making valuation of intangible assets vital for investors. (https://www.oceantomo.com/insights/ocean-tomo-releases-2015-annual-study-of-intangible-asset-market-value/) The Patent Valuation System estimates the profits generated by patents based on existing industry financial data and patent data, in order to minimize the subjective analysis involved. This method can be combined with any valuation method that requires an estimate of the expected returns generated by patents. Comparisons between the values generated by the patent valuation system and real-life values of actual patent transactions are carried out in order to gauge the system’s accuracy. Using PTR (price to technology ratio) ratio : The price-to-technology ratio (PTR) is the ratio for valuing a company that measures its current share price relative to its per-share technology value, where technology value of corporations is defined as the sum of value of patents they hold. PTR allows investors to make an investment decision in terms of technology value of corporations. PTR helps investors identify stocks that are overvalued or undervalued by comparing technology value to its stock price.

Techsalerator’s Business Technographic Data for North America is an invaluable resource designed to provide businesses, market analysts, and technology vendors with a comprehensive understanding of the technological landscape across North America. This dataset offers an in-depth examination of the technology ecosystems within companies operating in the region, offering a granular view into their technology stacks, digital tools, and IT infrastructure.

Key Features of the Dataset: Technology Stacks:

Detailed information on the technology stacks used by companies, including software, hardware, and platforms. This encompasses data on programming languages, frameworks, databases, cloud services, and enterprise applications that companies rely on. Digital Tools:

Insight into the digital tools and services adopted by businesses, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) solutions, collaboration tools, and marketing automation platforms. IT Infrastructure:

Data on the IT infrastructure of companies, including their network setups, server environments, data storage solutions, and cybersecurity measures. This also covers information on on-premises versus cloud-based infrastructure. Technological Trends:

Analysis of emerging technological trends and innovations being adopted across different sectors and regions. This helps in identifying shifts in technology usage and areas of growth within the North America market. Sector and Regional Breakdown:

Segmentation of data by industry sectors and geographic regions, providing insights into how technology adoption varies across different industries and North America countries.

North Countries Covered: Afghanistan Armenia Azerbaijan Bahrain Bangladesh Bhutan Brunei Cambodia China Cyprus Georgia India Indonesia Iran Iraq Israel Japan Jordan Kazakhstan Kuwait Kyrgyzstan Laos Lebanon Malaysia Maldives Mongolia Myanmar (Burma) Nepal North Korea Oman Pakistan Palestine Philippines Qatar Saudi Arabia Singapore South Korea Sri Lanka Syria Taiwan Tajikistan Thailand Timor-Leste (East Timor) Turkey Turkmenistan United Arab Emirates Uzbekistan Vietnam Yemen

Benefits of the Dataset: Strategic Insights: Businesses can leverage the data to make informed decisions about technology investments, partnerships, and competitive strategies based on a thorough understanding of the technology landscape. Market Analysis: Market analysts can use the data to identify trends, benchmark companies, and assess the technological capabilities of different sectors and regions. Vendor Strategy: Technology vendors can gain insights into the technology stacks and tools being used by potential clients, allowing them to tailor their offerings and sales strategies effectively. Techsalerator’s Business Technographic Data for North America equips stakeholders with essential information to navigate the complex technological environment of North America businesses, enabling more strategic and data-driven decisions.

Business dataset. Phone numbers, addresses and emails have been removed. This data came from an old database (over 10 years). Use as a practice dataset for Pandas, Pyspark or SQL. This dataset contains 784,156 records.

📈 Daily Historical Stock Price Data for American Battery Technology Company (2016–2025)

A clean, ready-to-use dataset containing daily stock prices for American Battery Technology Company from 2016-02-24 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: American Battery Technology Company Ticker Symbol: ABAT Date Range: 2016-02-24 to 2025-05-28 Frequency: Daily… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-american-battery-technology-company-20162025.

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

Comprehensive dataset of 16 Medical technology manufacturers in Tennessee, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

In 2024, Google's parent company reported an annual revenue increase of ** percent. In 2024, video content and streaming platform Netflix increased its annual revenue by ** percent. Meta Platforms (formerly Facebook Inc.) generated a ** percent year-on-year revenue increase during the same period. Additionally, Amazon had a year-over-year revenue increase of ** percent for its fiscal year of 2024.

This dataset is about book series. It has 1 row and is filtered where the books is Beatlemania : technology, business, and teen culture in cold war America. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Raw data which powers the Mapped In NY site at http://www.mappedinny.com/

This dataset contains location information for subsidiary operations of California clean technology companies in Mexico.

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated, and confidence is recorded for both. None of the data are field verified.

Success.ai’s Startup Data for Global Tech Startups offers a comprehensive and reliable dataset tailored for businesses, investors, and organizations seeking to connect with tech startups worldwide. Covering emerging companies in software, AI, fintech, health tech, and other innovation-driven industries, this dataset provides detailed funding insights, firmographic data, and verified contact details for decision-makers.

With access to continuously updated, AI-validated data from over 700 million global profiles, Success.ai ensures your outreach, partnership development, and investment strategies are powered by accuracy and relevance. Backed by our Best Price Guarantee, this solution is designed to help you thrive in the competitive global startup ecosystem.

Why Choose Success.ai’s Startup Data?

1. Verified Contact Data for Precision Outreach

   • Access verified contact details, including work emails and phone numbers, for startup founders, CEOs, and key decision-makers.
   • AI-driven validation ensures 99% accuracy, reducing errors and improving engagement outcomes.

2. Comprehensive Global Coverage

   • Includes tech startups from major hubs such as Silicon Valley, Europe’s fintech capitals, Asia’s innovation centers, and beyond.
   • Gain insights into startup operations, growth trajectories, and funding patterns across diverse regions.

3. Continuously Updated Datasets

   • Real-time updates capture changes in startup funding rounds, leadership roles, and business expansions.
   • Stay informed on emerging opportunities in the fast-evolving global startup landscape.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access comprehensive startup data for tech companies and professionals worldwide.
• Funding Insights: Understand investment rounds, funding amounts, and venture capital backing for tech startups.
• Firmographic Data: Gain details on company size, industry focus, business locations, and market presence.
• Decision-maker Profiles: Connect with founders, executives, and leadership teams driving innovation in global startups.

Key Features of the Dataset:

1. Comprehensive Startup Profiles

   • Identify and connect with tech startups specializing in AI, SaaS, fintech, health tech, e-commerce, and more.
   • Target decision-makers responsible for product innovation, market expansion, and fundraising strategies.

2. Advanced Filters for Precision Campaigns

   • Filter startups by funding stage (seed, Series A, Series B+), industry focus, or geographic location.
   • Tailor campaigns to align with specific business needs, such as partnership opportunities or technology solutions.

3. Regional and Industry-specific Insights

   • Leverage data on startup trends, funding activity, and market demands to refine strategies.
   • Align outreach efforts with high-growth sectors and regional opportunities in tech innovation.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Investor Relations and Partnership Development

   • Build relationships with startups seeking venture capital, strategic investments, or market partnerships.
   • Foster collaborations that enhance innovation, accelerate growth, or enter new markets.

2. Marketing Campaigns and Outreach

   • Promote technology solutions, consulting services, or operational tools tailored to the unique needs of startups.
   • Use verified contact data for targeted multi-channel outreach, including email, phone, and LinkedIn.

3. Market Research and Competitive Analysis

   • Analyze global startup trends, funding dynamics, and innovation hotspots to refine product development and marketing strategies.
   • Benchmark against competitors to identify high-demand solutions and underserved markets.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in tech startups seeking candidates for engineering, product management, or growth roles.
   • Provide workforce optimization platforms or talent development solutions tailored to startups.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality startup data at competitive prices, ensuring strong ROI for your marketing, sales, and investment initiatives.

2. Seamless Integration

   • Integrate verified startup data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost engagement rates in startup-focused campaigns.

4. Customizabl...

Context

Similar to others who have created HR data sets, we felt that the lack of data out there for HR was limiting. It is very hard for someone to test new systems or learn People Analytics in the HR space. The only dataset most HR practitioners have is their real employee data and there are a lot of reasons why you would not want to use that when experimenting. We hope that by providing this dataset with an evergrowing variation of data points, others can learn and grow their HR data analytics and systems knowledge.

Some example test cases where someone might use this dataset:

HR Technology Testing and Mock-Ups Engagement survey tools HCM tools BI Tools Learning To Code For People Analytics Python/R/SQL HR Tech and People Analytics Educational Courses/Tools

Content

The core data CompanyData.txt has the basic demographic data about a worker. We treat this as the core data that you can join future data sets to.

Please read the Readme.md for additional information about this along with the Changelog for additional updates as they are made.

Acknowledgements

Initial names, addresses, and ages were generated using FakenameGenerator.com. All additional details including Job, compensation, and additional data sets were created by the Koluit team using random generation in Excel.

Inspiration

Our hope is this data is used in the HR or Research space to experiment and learn using HR data. Some examples that we hope this data will be used are listed above.

Contact Us

Have any suggestions for additions to the data? See any issues with our data? Want to use it for your project? Please reach out to us! https://koluit.com/ ryan@koluit.com

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

Dataset

This dataset was created by Gaston Saracusti

Released under CC0: Public Domain

Contents

The rise in remote work and digital transformation initiatives has accelerated the demand for robust and scalable solutions offered by the database, storage and backup software publishing industry. Cloud adoption has surged, with downstream businesses in finance and healthcare increasingly relying on cloud-based databases and storage systems to ensure accessibility and resilience. To capture demand, publishers have grown revenue through subscription-based offerings, which have expanded the industry's reach and provided recurring revenue over the past five years. Driven by a 47.9% surge in 2021, industry revenue has increased at a CAGR of 10.2% to reach $98.9 billion, including growth of 2.5% in 2025. Advancements in cloud and digital technology have paved the way for new freemium substitutes, reshaping industry competition and introducing operational challenges. As new, cost-effective solutions emerge, traditional publishers have faced the challenge of differentiating their offerings while maintaining profitability. Leading companies such as Microsoft and Oracle have responded with investments in compatibility capabilities and AI features that have been designed to retain users as more options become available. Combined with the emerging threat of cyber attacks, however, these investments have weighed on industry profitability as greater resources are now needed to support different initiatives. With freemium models here to stay, industry revenue growth will decelerate moving forward. Users are expected to demand free tiers among leading publishers, who have already deployed these subscription models at the cost of revenue growth. Despite these trends, however, publishers are expected to benefit from data center expansions and upgrades, which will provide them with the necessary infrastructure to develop next-generation AI and edge computing offerings. With billions of dollars being invested in these areas, industry revenue will be sustained and rise at a CAGR of 2.5% over the next five years to reach $112.0 billion in 2030.