The dataset contains information about world's biggest companies.

Among them you can find companies founded in the US, the UK, Europe, Asia, South America, South Africa, Australia.

The dataset contains information about the year the company was founded, its' revenue and net income in years 2018 - 2020, and the industry.

I have included 2 csv files: the raw csv file if you want to practice cleaning the data, and the clean csv ready to be analyzed.

The third dataset includes the name of all the companies included in the previous datasets and 2 additional columns: number of employees and name of the founder.

In addition there's tesla.csv file containing shares prices for Tesla.

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...

This dataset provides comprehensive, up-to-date information about the top 100 Software-as-a-Service (SaaS) companies globally as of 2025. It includes detailed financial metrics, company fundamentals, and operational data that are crucial for market research, competitive analysis, investment decisions, and academic studies.

Key Features

• 100 leading SaaS companies across various industries
• 11 comprehensive data points per company
• Current 2025 data including latest valuations and ARR figures
• Verified information from multiple reliable sources
• Clean, analysis-ready format with consistent data structure

Use Cases

1. Market Research: Analyze SaaS industry trends and market dynamics
2. Investment Analysis: Evaluate growth patterns and valuation multiples
3. Competitive Intelligence: Benchmark companies within sectors
4. Academic Research: Study business models and growth strategies
5. Data Science Projects: Build predictive models for SaaS metrics
6. Business Strategy: Identify successful patterns in SaaS businesses

Industries Covered

Enterprise Software (CRM, ERP, HR) Developer Tools & DevOps Cybersecurity Data Analytics & Business Intelligence Marketing & Sales Technology Financial Technology Communication & Collaboration E-commerce Platforms Design & Creative Tools Infrastructure & Cloud Services

Why This Dataset? The SaaS industry has grown to over $300 billion globally, with companies achieving unprecedented valuations and growth rates. This dataset captures the current state of the industry leaders, providing insights into what makes successful SaaS companies tick.

Sources/Proof of Data: Data Sources The data has been meticulously compiled from multiple authoritative sources:

Company Financial Reports (Q4 2024 - Q1 2025)

Official earnings releases and investor relations documents SEC filings for public companies

Investment Databases

Crunchbase, PitchBook, and CB Insights for funding data Venture capital and private equity announcements

Market Research Reports

Gartner, Forrester, and IDC industry analyses SaaS Capital Index and valuation reports

Industry Publications

TechCrunch, Forbes, Wall Street Journal coverage Company press releases and official announcements

Product Review Platforms

G2 Crowd ratings and reviews Capterra and GetApp user feedback

Data Verification

Cross-referenced across multiple sources for accuracy Updated with latest available information as of May 2025 Validated against official company statements where available

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Success.ai’s Manufacturing Company Data and B2B Contact Data for Global Manufacturing Professionals empowers businesses to connect with key decision-makers in the manufacturing industry worldwide. With access to over 170 million verified professional profiles, this dataset includes critical contact information for executives, managers, engineers, and other professionals in manufacturing, supply chain, and production roles. Whether you're targeting plant managers, operations executives, or procurement officers, Success.ai ensures accurate and effective outreach.

Why Choose Success.ai’s Manufacturing Professionals Data?

1. Comprehensive Contact Information:
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for manufacturing leaders globally.

3. AI-validated data ensures 99% accuracy and up-to-date contact details for your outreach.

4. Global Reach Across Manufacturing Functions:

5. Includes profiles of manufacturing executives, plant managers, procurement specialists, engineers, and more.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets:

8. Real-time data updates guarantee you always have the latest information about manufacturing professionals.

9. Ethical and Compliant:

10. Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.

Data Highlights: - 170M+ Verified Professional Profiles: Includes manufacturing professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

Key Features of the Dataset:

1. Manufacturing Decision-Maker Profiles: Identify and connect with top-tier manufacturing professionals including operations leaders, plant managers, procurement officers, and senior executives.

2. Advanced Filters for Precision Targeting: Filter data by industry, company size, location, and specific job roles for optimal outreach.

3. AI-Driven Enrichment: Profiles enriched with actionable data to facilitate personalized engagement and higher success rates in campaigns.

Strategic Use Cases:

1. Supply Chain and Procurement Outreach:
2. Reach professionals involved in purchasing, supply chain management, and vendor relations.

3. Perfect for suppliers of equipment, materials, and logistics services to target key decision-makers in manufacturing.

4. Lead Generation for Manufacturing Solutions:

5. Promote manufacturing software, automation tools, and process optimization solutions.

6. Connect with professionals in charge of manufacturing operations to present cost-saving and efficiency-driving solutions.

7. Market Research and Industry Insights:

8. Gather data for industry trends, connect with thought leaders, and conduct targeted research in the global manufacturing sector.

9. Engage with professionals to build relationships and gain insights into evolving manufacturing practices.

10. Targeted Marketing Campaigns:

11. Design email marketing campaigns or direct outreach strategies targeting manufacturing decision-makers.

12. Utilize accurate contact data to drive higher engagement and conversion rates in your campaigns.

Why Choose Success.ai?

1. Best Price Guarantee: Enjoy the highest quality datasets at the most competitive pricing.

2. Seamless Integration: Easily integrate data into your CRM systems using APIs or download in the preferred format.

3. Data Accuracy with AI Validation: All profiles in this dataset are verified for 99% accuracy, ensuring confidence in the data for marketing, outreach, and decision-making.

4. Customizable and Scalable Solutions: Tailor the dataset to specific manufacturing sectors or job functions for more targeted outreach.

APIs for Enhanced Functionality:

1. Data Enrichment API: Enhance your existing records with verified manufacturing contact data to improve engagement and targeting.

2. Lead Generation API: Automate the lead generation process for manufacturing-specific campaigns to increase efficiency and scale.

Leverage Success.ai’s B2B Contact Data for Manufacturing Professionals to connect with key decision-makers in the global manufacturing industry. With verified emails, phone numbers, and continuously updated profiles, this data ensures that your outreach and communication efforts are impactful and precise.

Contact Success.ai now to elevate your manufacturing industry strategies with verified, AI-validated contact data. And remember—no one beats us on price. Period.

CompanyKG is a heterogeneous graph consisting of 1,169,931 nodes and 50,815,503 undirected edges, with each node representing a real-world company and each edge signifying a relationship between the connected pair of companies.

Edges: We model 15 different inter-company relations as undirected edges, each of which corresponds to a unique edge type. These edge types capture various forms of similarity between connected company pairs. Associated with each edge of a certain type, we calculate a real-numbered weight as an approximation of the similarity level of that type. It is important to note that the constructed edges do not represent an exhaustive list of all possible edges due to incomplete information. Consequently, this leads to a sparse and occasionally skewed distribution of edges for individual relation/edge types. Such characteristics pose additional challenges for downstream learning tasks. Please refer to our paper for a detailed definition of edge types and weight calculations.

Nodes: The graph includes all companies connected by edges defined previously. Each node represents a company and is associated with a descriptive text, such as "Klarna is a fintech company that provides support for direct and post-purchase payments ...". To comply with privacy and confidentiality requirements, we encoded the text into numerical embeddings using four different pre-trained text embedding models: mSBERT (multilingual Sentence BERT), ADA2, SimCSE (fine-tuned on the raw company descriptions) and PAUSE.

Evaluation Tasks. The primary goal of CompanyKG is to develop algorithms and models for quantifying the similarity between pairs of companies. In order to evaluate the effectiveness of these methods, we have carefully curated three evaluation tasks:

• Similarity Prediction (SP). To assess the accuracy of pairwise company similarity, we constructed the SP evaluation set comprising 3,219 pairs of companies that are labeled either as positive (similar, denoted by "1") or negative (dissimilar, denoted by "0"). Of these pairs, 1,522 are positive and 1,697 are negative.
• Competitor Retrieval (CR). Each sample contains one target company and one of its direct competitors. It contains 76 distinct target companies, each of which has 5.3 competitors annotated in average. For a given target company A with N direct competitors in this CR evaluation set, we expect a competent method to retrieve all N competitors when searching for similar companies to A.
• Similarity Ranking (SR) is designed to assess the ability of any method to rank candidate companies (numbered 0 and 1) based on their similarity to a query company. Paid human annotators, with backgrounds in engineering, science, and investment, were tasked with determining which candidate company is more similar to the query company. It resulted in an evaluation set comprising 1,856 rigorously labeled ranking questions. We retained 20% (368 samples) of this set as a validation set for model development.
• Edge Prediction (EP) evaluates a model's ability to predict future or missing relationships between companies, providing forward-looking insights for investment professionals. The EP dataset, derived (and sampled) from new edges collected between April 6, 2023, and May 25, 2024, includes 40,000 samples, with edges not present in the pre-existing CompanyKG (a snapshot up until April 5, 2023).

Background and Motivation

In the investment industry, it is often essential to identify similar companies for a variety of purposes, such as market/competitor mapping and Mergers & Acquisitions (M&A). Identifying comparable companies is a critical task, as it can inform investment decisions, help identify potential synergies, and reveal areas for growth and improvement. The accurate quantification of inter-company similarity, also referred to as company similarity quantification, is the cornerstone to successfully executing such tasks. However, company similarity quantification is often a challenging and time-consuming process, given the vast amount of data available on each company, and the complex and diversified relationships among them.

While there is no universally agreed definition of company similarity, researchers and practitioners in PE industry have adopted various criteria to measure similarity, typically reflecting the companies' operations and relationships. These criteria can embody one or more dimensions such as industry sectors, employee profiles, keywords/tags, customers' review, financial performance, co-appearance in news, and so on. Investment professionals usually begin with a limited number of companies of interest (a.k.a. seed companies) and require an algorithmic approach to expand their search to a larger list of companies for potential investment.

In recent years, transformer-based Language Models (LMs) have become the preferred method for encoding textual company descriptions into vector-space embeddings. Then companies that are similar to the seed companies can be searched in the embedding space using distance metrics like cosine similarity. The rapid advancements in Large LMs (LLMs), such as GPT-3/4 and LLaMA, have significantly enhanced the performance of general-purpose conversational models. These models, such as ChatGPT, can be employed to answer questions related to similar company discovery and quantification in a Q&A format.

However, graph is still the most natural choice for representing and learning diverse company relations due to its ability to model complex relationships between a large number of entities. By representing companies as nodes and their relationships as edges, we can form a Knowledge Graph (KG). Utilizing this KG allows us to efficiently capture and analyze the network structure of the business landscape. Moreover, KG-based approaches allow us to leverage powerful tools from network science, graph theory, and graph-based machine learning, such as Graph Neural Networks (GNNs), to extract insights and patterns to facilitate similar company analysis. While there are various company datasets (mostly commercial/proprietary and non-relational) and graph datasets available (mostly for single link/node/graph-level predictions), there is a scarcity of datasets and benchmarks that combine both to create a large-scale KG dataset expressing rich pairwise company relations.

Source Code and Tutorial:
https://github.com/llcresearch/CompanyKG2

Paper: to be published

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

Database Management System Market Outlook

According to our latest research, the global Database Management System (DBMS) market size reached USD 79.3 billion in 2024, demonstrating robust expansion with a CAGR of 13.2% from 2025 to 2033, and is forecasted to attain USD 236.8 billion by 2033. The market’s rapid growth is primarily driven by the exponential increase in data generation across industries, the rising adoption of cloud-based solutions, and the growing need for real-time data analytics and security. As organizations increasingly recognize the strategic value of data, DBMS solutions are becoming indispensable for efficient data storage, access, and management.

A major growth factor propelling the Database Management System market is the surge in digital transformation initiatives across both public and private sectors. Industries such as BFSI, healthcare, retail, and manufacturing are generating vast volumes of structured and unstructured data, necessitating sophisticated DBMS platforms for effective data handling. The proliferation of IoT devices, social media, and e-commerce platforms has further amplified the need for scalable and secure database solutions that can process diverse data types in real time. Additionally, the integration of artificial intelligence and machine learning with DBMS is enabling organizations to derive actionable insights, automate routine processes, and improve decision-making, thereby fueling market demand.

Another key driver is the shift towards cloud-based database management systems, which offer unparalleled flexibility, scalability, and cost efficiency compared to traditional on-premises solutions. Cloud DBMS platforms are particularly attractive to small and medium enterprises (SMEs) that lack the resources for extensive IT infrastructure investments, allowing them to leverage enterprise-grade data management capabilities on a subscription basis. Furthermore, with the advent of hybrid and multi-cloud environments, organizations can now optimize their data architecture for performance, redundancy, and compliance, further accelerating the adoption of cloud DBMS solutions globally.

Regulatory compliance and data security concerns are also catalyzing the growth of the Database Management System market. Governments and industry bodies worldwide are introducing stringent regulations around data privacy, storage, and access, compelling organizations to upgrade their database infrastructure. Advanced DBMS solutions now incorporate robust encryption, granular access controls, and automated compliance monitoring, ensuring that sensitive data is protected and regulatory obligations are met. This heightened focus on data governance is prompting enterprises to invest in next-generation DBMS technologies, thereby expanding the market’s growth trajectory.

Regionally, North America continues to dominate the Database Management System market owing to its advanced IT infrastructure, strong presence of leading market players, and early adoption of emerging technologies. Europe follows closely, driven by stringent data protection regulations and increasing digitalization across industries. The Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, burgeoning IT and telecom sectors, and a rising number of SMEs embracing cloud-based solutions. Latin America and the Middle East & Africa are also experiencing steady growth, supported by expanding internet penetration and government-led digital initiatives. This regional diversity ensures that the DBMS market remains dynamic and resilient to global economic fluctuations.

Component Analysis

The Database Management System market is distinctly segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment, which encompasses both relational and non-relational DBMS platforms, forms the backbone of the market and accounts for the majority of revenue share. This dominance is attributed to the conti

Analysis of ‘China Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/china-largest-companiese on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 100 samples along with Profits ($billion), Market Value ($billion), technical information and other features such as: - Sales ($billion) - Assets ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Market Value ($billion) on Sales ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Greece Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/greece-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Market Value ($billion), Assets ($billion), technical information and other features such as: - Profits ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Assets ($billion) in relation to Profits ($billion)
• Study the influence of Market Value ($billion) on Assets ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Database Market Outlook

The global database market size was valued at approximately USD 67 billion in 2023 and is projected to reach USD 138 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3%. The market is poised for significant growth due to the increasing demand for data storage solutions and the rapid digital transformation across various industries. As businesses continue to generate massive volumes of data, the need for efficient and scalable database solutions is becoming more critical than ever. This growth is further propelled by advancements in cloud computing and the increasing adoption of artificial intelligence and machine learning technologies, which require robust database management systems to handle complex data sets.

One of the primary growth factors for the database market is the exponential increase in data generation from various sources, including social media, IoT devices, and enterprise applications. As organizations strive to leverage data for competitive advantage, the demand for sophisticated database technologies that can manage, process, and analyze large volumes of data is on the rise. These technologies enable businesses to gain actionable insights, improve decision-making, and enhance customer experiences. Additionally, the proliferation of connected devices and the Internet of Things (IoT) are contributing to the surge in data volume, necessitating the deployment of advanced database systems to handle the influx of information efficiently.

The cloud computing revolution is another significant growth driver for the database market. With the increasing adoption of cloud-based services, organizations are shifting from traditional on-premises database solutions to cloud-based database management systems. This transition is driven by the need for scalability, flexibility, and cost-effectiveness, as cloud solutions offer the ability to scale resources up or down based on demand. Cloud databases also provide enhanced data security, disaster recovery, and backup solutions, making them an attractive option for businesses of all sizes. Moreover, cloud service providers continuously innovate by offering managed database services, reducing the burden on IT departments and allowing organizations to focus on core business activities.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also playing a crucial role in shaping the future of the database market. These technologies require robust and dynamic database systems capable of handling complex algorithms and large data sets. Databases optimized for AI and ML applications enable organizations to harness the power of predictive analytics, automation, and data-driven decision-making. The integration of AI and ML with database systems enhances the ability to identify patterns, detect anomalies, and predict future trends, further driving the demand for advanced database solutions.

From a regional perspective, North America is expected to dominate the database market, owing to the presence of established technology companies and the rapid adoption of advanced technologies. The region's mature IT infrastructure and the increasing need for data-driven insights in various industries contribute to the market's growth. Asia Pacific is anticipated to witness the highest growth rate during the forecast period, driven by the increasing digitization efforts, rising internet penetration, and the growing popularity of cloud-based solutions. Europe is also expected to experience significant growth due to the expanding IT sector and the increasing adoption of data analytics solutions across industries.

Type Analysis

The database market can be segmented by type into relational, non-relational, cloud, and others. Relational databases are among the oldest and most established types of database systems, widely used across industries due to their ability to handle structured data efficiently. These databases rely on structured query language (SQL) for managing and manipulating data, making them suitable for applications that require complex querying and transaction processing. Despite their maturity, relational databases continue to evolve, with advancements such as NewSQL and distributed SQL databases enhancing their scalability and performance for modern applications.

Non-relational databases, also known as NoSQL databases, have gained popularity in recent years due to their flexibility and ability to handle unstructured data. These databases are designed to accommodate a diverse range of data types, making them ideal for applications involving large v

Analysis of ‘Germany Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/germany-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Profits ($billion), Assets ($billion), technical information and other features such as: - Sales ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Global Rank in relation to Profits ($billion)
• Study the influence of Assets ($billion) on Sales ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

In-Memory Database Market size was valued at USD 9.84 Billion in 2024 and is projected to reach USD 35.52 Billion by 2031, growing at a CAGR of 19.20% during the forecast period 2024-2031.

Global In-Memory Database Market Drivers

Demand for Real-Time Analytics: Companies are depending more and more on real-time data to make prompt, well-informed choices. Because they speed up data processing, in-memory databases are crucial for real-time analytics applications. Growth of Big Data and IoT: Large volumes of data are generated by the spread of big data and the Internet of Things (IoT), which must be quickly processed and analyzed. Large data volumes can be handled by in-memory databases more effectively than by conventional disk-based databases. Both Scalability and Performance Requirements: Databases that can scale to accommodate growing data loads without sacrificing performance are essential for growing enterprises. Growing businesses can benefit from the great scalability and performance of in-memory databases. Developments in Memory Technologies: As memory technologies like RAM and flash memory continue to progress, in-memory databases are becoming more widely available and reasonably priced for a greater variety of uses. Quicker Decision-Making Is Required: Businesses must act fast in the current competitive environment in order to stay ahead. Decision-making processes can go more quickly because to in-memory databases' faster data access and processing speeds. Demand for Real-Time Personalization: To improve consumer experiences, real-time personalization is becoming more and more necessary as e-commerce and online services expand in popularity. Large volumes of client data may be instantly analyzed by in-memory databases, allowing them to provide tailored content and recommendations.

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.

Analysis of ‘Brazil Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/brazil-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Assets ($billion), Sales ($billion), technical information and other features such as: - Profits ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Assets ($billion) in relation to Sales ($billion)
• Study the influence of Profits ($billion) on Market Value ($billion)
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

This Private Company Data dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B private company data. This data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading private company data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

NOTE: The 2018 PREDICT Dataset has been deprecated, and it is now superseded by its latest edition - 2019 PREDICT Dataset:

http://data.europa.eu/89h/6c6f7ce7-893b-48e9-b074-2baaa4b6c7d8

PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU28 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia -. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure. Nowcasting of more relevant data in these domains is also performed until a year before the reference date, while time series go back to 1995.

ICTs determine competitive power in the knowledge economy. The ICT sector alone originates almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; and the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015.

United States Gross Value Added (GVA): saar data was reported at 19,931.717 USD bn in Mar 2018. This records an increase from the previous number of 19,699.332 USD bn for Dec 2017. United States Gross Value Added (GVA): saar data is updated quarterly, averaging 5,305.278 USD bn from Mar 1959 (Median) to Mar 2018, with 237 observations. The data reached an all-time high of 19,931.717 USD bn in Mar 2018 and a record low of 517.130 USD bn in Mar 1959. United States Gross Value Added (GVA): saar data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB074: Integrated Macroeconomic Accounts: Total Economy and Sectors: Selected Aggregates.

This table provides an overview of the non-financial transactions of the institutional sectors of the Dutch economy, distinguishing between uses and resources. Non-financial transactions consist of current transactions and transactions from the capital account. Furthermore, this table provides the main balancing items of the (sub)sectors. Non-financial transactions are estimated for the main institutional sectors of the economy and the rest of the world. Sectors are presented both consolidated and non-consolidated.

Data available from: Annual figures from 1995. Quarterly figures from first quarter 1999.

Status of the figures: Annual figures from 1995 up to and including 2023 are final. Quarterly data from 2023 are provisional.

Changes as of June 24th, 2025: Data on the first quarter of 2025 have been added. Following revision policy, 2023 and 2024 data are updated, and time series of the sector accounts are revised (annual revision).

Adjustment as of April 10th 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. We refer to the Government Finance Statistics for the current figures. Links to the Government Finance Statistics could be found in paragraph 3. Until the publication end of June the Sector accounts therefore diverge from the Government Finance Statistics.

Adjustment as of July 12th 2024: Total consolidated resources and uses are adjusted for most sectors, due to a calculation error. For the sector rest of the world, the non-consolidated total resources and uses have also been adjusted. Imports and exports of goods and services were wrongly not included in the total resources and uses. For the sectors non-financial corporations and financial corporations, capital taxes (uses) were wrongly shown as empty cell (figure not applicable).

When will new figures be published? Annual figures: The first annual data are published 85 day after the end of the reporting year as the sum of the four quarters of the year. Subsequently provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Furthermore the sector accounts are annually revised for all reporting periods. These data are published each year in June. Quarterly figures: The first quarterly estimate is available 85 days after the end of each reporting quarter. The first quarter may be revised in September, the second quarter in December. Should further quarterly information become available thereafter, the estimates for the first three quarters may be revised in March. If (new) annual figures become available in June, the quarterly figures will be revised again to bring them in line with the annual figures. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest figures. Revised yearly figures are published in June each year.