Bright Data’s datasets are created by utilizing proprietary technology for retrieving public web data at scale, resulting in fresh, complete, and accurate datasets. CrunchBase datasets provide unique insights into the latest industry trends. They enable the tracking of company growth, identifying key businesses and professionals, tracking employee movement between companies, as well as enabling more efficient competitive intelligence. Easily define your Crunchbase dataset using our smart filter capabilities, enabling you to customize pre-existing datasets, ensuring the data received fits your business needs. Bright Data’s Crunchbase company data includes over 2.8 million company profiles, with subsets available by industry, region, and any other parameters according to your requirements. There are over 70 data points per company, including overview, details, news, financials, investors, products, people, and more. Choose between full coverage or a subset. Get your Crunchbase dataset Today!

1) Data Introduction • The Crunchbase company Data includes a variety of startup-related data, including startup enterprise information, year of establishment, investment history, and industrial sector released by Crunchbase.

2) Data Utilization (1) Crunchbase company Data has characteristics that: • This dataset provides a variety of attributes that can impact the growth and success of a start-up, including company name, year of establishment, location, industry, investment stage, investment amount, and investors. (2) Crunchbase company Data can be used to: • Development of Startup Success Prediction Model: By utilizing key attributes such as the year of establishment, investment history, and industrial sector, we can build a model that predicts a startup's chances of success. • Venture investment and market analysis: It can be used to identify venture investment strategies and market trends by analyzing startup distribution and growth patterns by investment stage and industry.

The CrunchBase Open Data Map is our answer for anyone that wants to reference CrunchBase or include basic CrunchBase profile information within their application. The data map includes the core CrunchBase information for People and Organizations in the CrunchBase Dataset.

Traffic analytics, rankings, and competitive metrics for crunchbase.com as of June 2025

crunchbase.com is ranked #4527 in US with 5.68M Traffic. Categories: Computer Software and Development, Online Services. Learn more about website traffic, market share, and more!

Explore the historical Whois records related to crunchbase.club (Domain). Get insights into ownership history and changes over time.

This datasource seems to be permanently down. A potential pointer might be this tweet from the maintainer: https://twitter.com/#!/bengee/status/90414011685941248:

"another dependency (before #job search) removed: semsol.com now redirects to bnowack.de"

A CrunchBase wrapper to Linked Data

Juul Labs, the company that develops and produced JUUL e-cigarettes, has collected investments totaling **** billion U.S. dollars. In 2020, Altria, the company behind Marlboro, acquired a 35 percent stake in Juul Labs. As of 2023, Juul is struggling in the United States due to regulatory issues. The Food and Drug Administration banned JUUL e-cigarettes in the U.S. in June of 2022, this ban was reversed at the beginning of June of 2024.

The Venture Capital (VC) New Media Service Platform market, currently valued at $2.199 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 15.9% from 2025 to 2033. This surge is driven by several key factors. The increasing reliance of Venture Capital firms on data-driven decision-making fuels demand for sophisticated platforms providing comprehensive market intelligence, deal sourcing, and portfolio management tools. Furthermore, the rising number of startups and the expanding VC landscape create a fertile ground for specialized new media services that streamline due diligence, improve investor relations, and enhance overall operational efficiency. The integration of artificial intelligence (AI) and machine learning (ML) within these platforms further enhances their value proposition, offering predictive analytics and automated processes. Competition within the sector is intense, with prominent players like TechCrunch, Crunchbase, AngelList, The Information, CB Insights, LinkedIn, and PitchBook vying for market share through innovative features and strategic partnerships. This competitive environment pushes continuous product development and fosters market innovation. The market's future growth hinges on factors such as technological advancements, the continued evolution of the VC industry, and the ability of platforms to adapt to changing investor preferences. Challenges may include maintaining data accuracy, ensuring platform security, and managing regulatory compliance within the rapidly evolving fintech space. While the historical period (2019-2024) likely saw lower growth rates than the projected CAGR, the acceleration expected between 2025 and 2033 reflects the maturation of the market and the increasing adoption of these essential tools by both startups and VCs. The segment breakdown, though not explicitly provided, could reasonably encompass features such as deal flow management, fundraising support, investor network building, and market intelligence tools. Geographic expansion into emerging VC markets will also likely play a significant role in driving future growth.

Data Fusion of: Companies: RAW - Companies House Records; Raw - Relatorios de Contas (act Nov 2016); RAW - Crunchbase and Webpages and FINOVA FCR´s: Raw - Relatorios de Contas (act Nov 2016); RAW - Crunchbase and Webpages and FINOVA

Export of EDI_SubGrantees_DB at the end of the second reporting period (M27). It includes the data available at M27, since the 2nd round of incubation actually finishes in M28. Only non-privacy sensitive fields from EDI_SubGrantees_DB will be dumped in this export. This dataset corresponds to the following statement encountered it the Grant Agreement: “EDI will publish an Open Dataset with the beneficiaries of our three open calls including their data and funding received. This dataset will be made available at FIWARE Lab. This has been proven as a beneficial measure for transparency but also discoverability of the companies and their investment received in EDI. At the same time, we will populate the wiki-sourced database of Crunchbase to facilitate access to the data of our start-ups and the incubator as such in one of the reference portals in the start-up ecosystem”.

Export of EDI_SubGrantees_DB at the end of the first reporting period (M15), i.e. March 2019. It includes the data available at M15, since the 1st round of incubation actually finishes in M16. Only non-privacy sensitive fields from EDI_SubGrantees_DB will be dumped in this export.

This dataset corresponds to the following statement encountered it the Grant Agreement: “EDI will publish an Open Dataset with the beneficiaries of our three open calls including their data and funding received. This dataset will be made available at FIWARE Lab. This has been proven as a beneficial measure for transparency but also discoverability of the companies and their investment received in EDI. At the same time, we will populate the wiki-sourced database of Crunchbase to facilitate access to the data of our start-ups and the incubator as such in one of the reference portals in the start-up ecosystem”.

Invoice automation software received several investments in recent years, with AvidXchange having had over ***** times more money than Bill.com before its IPO. Software solutions to automate processes surrounding accounts payable and receivable - like processing large volumes of invoices or managing financial transactions from one company to its suppliers - is increasingly seen as one of the more urgent aspects of digital B2B payments. This became especially prevalent in the wake of the COVID-19 pandemic, as many companies were suddenly unable to pay their bills on time. In Germany, for instance, the average number of days before a B2B transactions was settled in Germany grew from ** days in 2019 to more than three months in 2020 - a figure substantially higher than the average number of days for a B2B payment in other European countries.

These data were generated as part of a two-and-a-half-year ESRC-funded research project examining the digitalisation of higher education (HE) and the educational technology (Edtech) industry in HE. Building on a theoretical lens of assetisation, it focused on forms of value in the sector, and governance challenges of digital data. It followed three groups of actors: UK universities, Edtech companies, and investors in Edtech. The researchers first sought to develop an overview of the Edtech industry in HE by building three databases on Edtech companies, investors in Edtech, and investment deals, using data downloaded from Crunchbase, a proprietary platform. Due to Crunchbase’s Terms of Service, only parts of one database are allowed to be submitted to this repository, i.e. a list of companies with the project’s classification. A report offering descriptive analysis of all three databases was produced and is submitted as well. A qualitative discursive analysis was conducted by analysing seven documents in depth. In the second phase, researchers conducted interviews with participants representing three groups of actors (n=43) and collected documents on their organisations. Moreover, a list of documents collected from Big Tech (Microsoft, Amazon, and Salesforce) were collected to contextualise the role of global digital infrastructure in HE. Due to commercial sensitivity, only lists of documents collected about investors and Big Tech are submitted to the repository. Researchers then conducted focus groups (n=6) with representatives of universities (n=19). The dataset includes transcripts of focus groups and outputs of writing by participants during the focus group. Finally, a public consultation was held via a survey, and 15 participants offered qualitative answers.

The Asset Preservation Solutions market is experiencing robust growth, driven by increasing digitalization, rising data volumes, and the growing need for data security and regulatory compliance across various industries. The market's expansion is fueled by the adoption of advanced technologies like cloud computing, artificial intelligence (AI), and blockchain, which enhance data protection and disaster recovery capabilities. While precise figures are unavailable, considering a typical CAGR of 10-15% for rapidly growing tech markets and a starting market size of $50 billion in 2025, we can project substantial growth over the forecast period (2025-2033). This growth is not uniform across all segments. For instance, the demand for cloud-based asset preservation solutions is surging due to its scalability and cost-effectiveness, while on-premise solutions retain relevance for organizations with stringent data sovereignty requirements. Key players like Crunchbase, 6Sense, and Tracxn Technologies are leading the market innovation, driving competition and technological advancements. However, the market also faces challenges such as high implementation costs, integration complexities, and the need for skilled professionals, which can act as potential restraints on growth in certain sectors. Geographical distribution shows a strong concentration in North America and Europe, driven by early adoption of advanced technologies and stringent data protection regulations. Asia-Pacific is expected to exhibit significant growth in the coming years, fueled by increasing digitalization and a growing awareness of data security risks. The competitive landscape is dynamic, with both established players and innovative startups vying for market share. The market's future trajectory is likely to be influenced by evolving regulations, technological advancements, and the expanding adoption of asset preservation solutions across diverse sectors, including finance, healthcare, and government. Strategic partnerships and acquisitions will likely play a significant role in shaping the market landscape in the future. Focusing on niche segments and delivering tailored solutions will be crucial for success in this competitive and evolving market.

This is a dataset of the datasets on the site. It is updated daily.

This is a dataset of the datasets on the site. It is updated daily.

Company Intelligence Name and Websites - Company Website and Alternative Domains.
Address - Standardized headquarter Address, City, Region, Zip Code, and Country LAT / LONG - Used for Geo Location Locations - Additional office locations of the business Phone - Standardized headquarter phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, Twitter, Yelp, Instagram Type - Headquarters, Branch, Local Only Description - detailed overview of the company business model and pursuit. Industry - Standardized Industries to segment companies by their most notable contributions Sector - 20 industry groupings Specialties - Non industry details shared by the company to better understand what they do SIC Code - 839 industry classifications and their definitions Revenue - Annual revenue from 1M to over 1B Employee - Number of Employees at the company

Similar Companies - used to identify competitors Funding - for start up data IP Address - from the hosted website Affiliated Companies - company hierarchy

Developers were asked: "How do you plan to fund application development and maintenance?"