Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Get access to information about all apps in the Google Playstore to understand your competitors, market to app developers etc. This dataset includes all the fields available in the play store such as:

• Name, description, rating information etc.
• Technical information such as size, app version etc.
• Permissions.
• Developer information.
• Contact information.
• Parsed app-ads.txt information for publisher domains.
• Reviews (more than 100 million reviews available)

Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.

Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.

Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.

Dataset Features

• url: The URL linking to the app’s page on the Apple App Store.
• title: The name of the app.
• sub_title: A brief subtitle or tagline for the app.
• developer: The name of the entity or individual that developed the app.
• top_charts: Indicates if the app appears in top charts.
• monetization_features: Information on monetization aspects (such as in-app purchases or advertisements).
• image: A reference to the main app image.
• screenshots: Contains screenshot images of the app.
• description: Detailed app description outlining main features.
• what_new: Details on the latest updates or new features.
• rating: The overall rating based on user reviews.
• number_of_raters: The total number of users who have rated the app.
• reviews_by_stars: Breakdown of the number of reviews by star rating.
• reviews: An aggregation of user reviews.
• events: Any associated events or promotions.
• data_linked_to_you: Indicates if any data is linked to the user.
• seller: The entity responsible for selling or distributing the app.
• category: The category or genre of the app.
• languages: Languages supported by the app.
• copyright: Copyright information provided by the developer.
• size: The file size of the app.
• compatibility: Device or OS compatibility details.
• age_rating: The recommended age rating for the app.
• price: The price of the app.
• In_app_purchases: Details on in-app purchase options.
• support: Information related to app support.
• more_by_this_developer: Suggestions for other apps by the same developer.
• you_might_also_like: Recommendations for similar apps.
• app_support: Additional support details.
• privacy_policy: Link or reference to the app’s privacy policy.
• developer_website: The website of the app developer.
• featured_in: Information on any features or showcases the app has being part of.
• country: The country from which the app’s data was sourced.
• timestamp: A timestamp indicating when the data record was last updated.
• latest_app_version: The most recent version of the app available.
• app_id: A unique identifier for the app.

Distribution

• Data Volume: 36 Columns and 68M Rows
• Format: CSV

Usage

This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.

Coverage

• Geographic Coverage: Global

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: Can leverage this dataset for training machine learning algorithms and building predictive models concerning app tr

As of the first quarter of 2022, it was found that approximately 76 percent of the leading mobile apps for children hosted in the Google Play Store could potentially transmit location data to advertisers, while in the case of the Apple App Store the number of popular children apps corresponded to around 67 percent of the total. Additionally, 75 percent of iOS apps had potential access to personal data through permissions, while in the case of apps hosted in the Google Play Store the number of popular children apps holding the same possibility corresponded to around 35 percent of the total.

Dataset Card for Macappstore Applications Metadata

Mac App Store Applications Metadata sourced by the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This data aims to cover our internal company research needs and start collecting and sharing the macOS app dataset since we have yet to find a suitable existing one. Full application metadata was sourced by the public iTunes search API for the US, Germany, and Ukraine… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-metadata.

As of May 2025, nearly 97 percent of apps in the Google Play app store were freely available. The number of free apps on the Google Play Store and the Apple Store alike has been consistently higher than the number of paid apps. By comparison, free Android apps on Amazon Appstore were roughly 81 percent, while paid apps accounted for a share of 19 percent of the total apps available in the store. Mobile apps and consumer spending Mobile apps have become integral to our daily routine, offering convenience and entertainment. In the second quarter of 2024, the total value of the global consumer spending on mobile apps was almost 35 billion U.S. dollars, highlighting the significant role that mobile apps play in the digital economy. As of the third quarter of 2023, consumers spent an average of 5.05 U.S. dollars on mobile apps per smartphone, which underlines the high demand for these digital solutions. App stores commission rates under scrutiny As of August 2023, the standard commission rates on revenues generated from apps hosted on the Apple App Store and the Google Play Store were set at 30 percent. However, between the end of 2020 and mid-2021, both Apple and Google were forced to address the criticism of their app store policies. In 2020, the European Union drafted the Digital Market Act, with the purpose of ensuring a healthy degree of competition in the tech environment. In December 2022, Apple was reported to start planning to allow sideloading and the presence of alternative app stores on its devices. In August 2021, the United States Senate presented the Open Apps Market Act to reduce tech giants‘ control over the digital app market. As regulations are expected to promote competition in the tech and mobile environment, in March 2023, Microsoft was reported to preparing to launch a new mobile gaming store, which will compete with the Apple App Store and the Google Play Store.In 2026, mobile app spending is forecasted to reach 161 billion U.S. dollars and 72 billion U.S. dollars on the Apple App Store and the Google Play Store, respectively. While both Google and Apple started applying some changes in their app store policies in 2021, like lowering commission fees for small publishers generating less than 1 million U.S. dollars in yearly revenues, the two tech giants might face additional restrictions and limitations in all their major markets. In the case of Apple, in 2021, the company updated its App Store policies, allowing developers to offer alternative payment methods. In 2022, Apple updated its review guidelines, requiring developers to share more information about collecting and using data, including disclosing the types of collected data and how it's used.

App Revenue Key StatisticsMobile Ad SpendApp and Game RevenuesiOS App and Game RevenueGoogle Play App and Game RevenueGaming App RevenuesiOS Gaming App RevenueGoogle Play Gaming App RevenueApp...

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Enterprise App Store Market Size 2024-2028

The enterprise app store market size is forecast to increase by USD 4.59 billion at a CAGR of 18.51% between 2023 and 2028.

The market is witnessing significant growth due to the increasing need to enhance business efficiency and productivity. The integration of advanced technologies such as artificial intelligence (AI), analytics, and machine learning into enterprise resource planning (ERP) software, hybrid cloud, and other enterprise application software is driving market growth. Additionally, the integration of blockchain technology is expected to provide enhanced security and transparency to enterprise applications. Digital transformation is another key trend In the market, with organizations increasingly adopting mobile apps for various business functions, including logistics, e-commerce, CRM, and ERP. The integration of deep learning and AI in mobile applications is enabling predictive analytics and automation, leading to improved business outcomes.

What will be the Size of the Enterprise App Store Market During the Forecast Period?

Request Free Sample

The market continues to experience significant growth as large enterprises and Small and Medium-sized Enterprises (SMEs) increasingly adopt mobile application development for digitization. Internal app marketplaces have emerged as a key trend, enabling organizations to distribute and manage mobile applications internally. Bring Your Own Device (BYOD) policies have further fueled this trend, with asset teams facilitating the deployment of enterprise mobility solutions. Patent activity In the mobile application development space reflects the market's dynamic nature, with businesses seeking to protect their intellectual property. Version control and self-service mobile applications are also gaining traction, allowing for efficient management and customization of business applications.
Moreover, enterprise mobility solutions encompass both on-premise and cloud deployment models, catering to various organizational needs. The market spans various industries, including IT, retail and e-commerce, health and fitness, and more. Confidential information security and strong security features remain paramount, as enterprise app stores increasingly handle sensitive business data. While gaming, music and entertainment, and social networking apps may be popular consumer categories, the market primarily focuses on business applications. Android is a dominant platform, though other operating systems also find use in specific enterprise contexts. Overall, the market shows no signs of slowing down, as businesses continue to leverage mobile technology for increased productivity and efficiency.

How is this Enterprise App Store Industry segmented and which is the largest segment?

The enterprise app store industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premise


Type

  Large enterprise
  SME


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Deployment Insights

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

The enterprise app market encompasses cloud-based and on-premises app stores, catering to the needs of large enterprises and SMEs across industries, including IT, BFSI, and retail. Cloud-based enterprise app stores dominate the market due to their ability to offer centralized control, automation, and optimization of business processes for global organizations. This trend is particularly prominent in developing countries with increasing SME presence, such as India and China. Mobile application development in areas like Android and iOS mobility solutions, version control, and customizability is a significant driver for enterprise mobility solutions. Security features, including multi-factor authentication, encryption, and tamper-proofing, are essential considerations for these platforms, ensuring confidential information remains secure during remote work.

Get a glance at the Enterprise App Store Industry report of share of various segments Request Free Sample

The cloud segment was valued at USD 1.77 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 31% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market is currently driven by significant inves

This dataset contains fictional reviews from a hypothetical mobile application, generated for demo purposes in various projects. The reviews include detailed feedback from users across different countries and platforms, with additional attributes such as star ratings, like/dislike counts, and issue flags. The data was later used as an input for a large language model (LLM) to generate labeled outputs, which are included in a separate dataset named labeled_app_store_reviews. This labeled dataset can be used for machine learning tasks such as sentiment analysis, text classification, or even A/B testing simulations.

In July 2024, Shazam: Find Music & Concerts was the most popular app published by Apple, generating approximately 4.6 million downloads from iOS and Google Play Store users during the month. Second-ranked Move to iOS was downloaded three million times from global users. Developer-specific app TestFlight, which allows app publishers to test beta versions of their product to publish on the Apple App Store was the third most popular app published by Apple, with 1.66 million downloads

Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

Global App Store Optimization Software market size expected from $3.86 Bn in 2023 to $8.66 Bn by 2032, at CAGR 9.40% during forecast period (2024-2032)

About the Dataset

Context While there are numerous public datasets available, particularly for the Apple App Store (on platforms like Kaggle), there is a noticeable lack of similar datasets for Google Play Store apps. After investigating further, I discovered that the iTunes App Store utilizes a well-organized, index-like structure for easy web scraping. However, Google Play Store relies on more complex modern techniques such as dynamic page loading using JQuery, making it more difficult to scrape the data.

Content Each entry (representing an app) contains attributes like category, rating, size, and other relevant details.

Acknowledgements This dataset was sourced from web scraping the Google Play Store. Without this, the app data would not have been accessible.

Inspiration The data from the Google Play Store offers great potential for driving success in the app development industry. Developers can extract valuable insights to enhance their offerings and effectively tap into the Android market!

A vast collection of data which includes the Top 100 Free Applications in the iOS App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the top free app chart in the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Dataset for the paper A Longitudinal Study of Removed Apps in iOS App Store (WWW 2021)

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

As of May 2023, product interaction data were the most commonly collected data points, with 94 over the 100 analyzed apps reporting to collect such data. User ID and crash data were collected by by 93 and 92 apps over 100, respectively. Over the 10 leading shopping apps hosted on the Apple App Store, the totality collected precise location, physical address, and payment info.

App Store Optimization Software Market size was valued at USD 0.53 Billion in 2024 and is projected to reach USD 1.99 Billion by 2032, growing at a CAGR of 18.02% from 2026 to 2032.

The App Store Optimization (ASO) Software market is driven by the rapidly growing number of mobile applications and the increasing competition among app developers to enhance their visibility and downloads. As businesses increasingly recognize the importance of mobile apps in their digital strategy, the demand for tools that improve app discoverability, user engagement, and conversion rates intensifies. The rise in mobile app usage, coupled with the necessity for better app ranking on app stores like Google Play and Apple App Store, propels the adoption of ASO software. Additionally, the integration of advanced analytics, keyword optimization, and user behavior insights within ASO tools enhances app performance and ROI, further fueling market growth. The continuous updates and algorithm changes in app stores also necessitate the use of specialized software to stay competitive, driving the market forward.

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.