This statistic shows the operating system share of enterprise endpoint devices in North America, Western Europe and Asia Pacific, as of 2022. At that time, the Windows operating system was running on **** percent of enterprise endpoint devices in these regions.

In 2024, over ** percent of developers worldwide reported using the Windows operating system for personal use, but only ** percent reported using the OS when working. MacOS, based on Unix, had the second-largest share at ** percent of developers using it for both personal and professional use, respectively. Linux dominates the rest of the market Among developers, Linux-based operating systems are highly favored for their flexibility and robust development environments, with Ubuntu ranking as the third most widely used distribution globally. Furthermore, other Linux distributions gained significant traction, such as Debian, Arch, Fedora, and Google’s ChromeOS among others. Android as the top mobile OS for developers When it came to mobile operating systems, Android had the highest percentage of developers using it both personally and professionally, while Apple's iOS and iPadOS held significantly lower shares. This trend extends beyond software development; Android's appeal is further highlighted by the fact that over one in 4 game developers globally report it as their preferred gaming platform, making it also one of the most popular platforms for game development worldwide.

Windows 10 is the most popular Windows desktop operating system, accounting for a market share of around ** percent as of March 2025. The share of devices running the older Windows 7 OS has slipped over the past year, with the newer Windows ** running on around ** percent of devices. A global dominance in computer operating systems Microsoft’s Windows is the most widely used computer (desktop, tablet, and console) operating system (OS) in the world with more than ** percent of the market share. It's closest competitors — Apple's macOS and iOS — hold a combined less than ** percent of the market. Despite its dominance in the computer OS market, Microsoft has been unable to have the same impact on the mobile OS market, where Google’s Android and Apple’s iOS split the market with a combined share of over ** percent. Operating systems   Operating systems are the underlying platforms connecting computer hardware and software. They provide users with the graphical interface that enables them to issue commands and perform tasks on electronic devices. Almost every device today — from smartphones and desktop computers, to graphical calculators — requires an operating system to function.

Microsoft's Windows was the dominant desktop operating system (OS) worldwide as of March 2025, with a market share of around ** percent. Apple’s Mac operating system has gained market share over the years, growing to command around a fifth of the market. Linux and Google's Chrome OS have retained small but stable market shares in recent years. Different versions of Microsoft Windows From its initial release in 1985, Microsoft Windows has gone through endless mutations. Notable versions include Windows 95, Windows XP, and Windows 7. Windows 11 is the newest addition to the family, being able to run on PCs, tablets and embedded devices. In 2022, approximately ** million PCs were shipped with Windows operating systems installed. Apple’s Mac operating system With an equally long history, Apple’s Mac operating system (macOS, previously Mac OS X and OS X) has also evolved through numerous releases. MacOS Ventura is the nineteenth release of macOS. A older version of macOS, Catalina, is currently the most popular macOS, now run on **** percent of Apple computers as of January 2023. macOS runs on Apple’s Mac computers, including the MacBook, which is Apple’s laptop PC product including the MacBook Pro and MacBook Air, and the iMac – Apple’s desktop computer.

Introduction

Operating System Statistics: ​Operating systems (OS) are integral to modern computing, facilitating user interaction with hardware across devices such as desktops, laptops, tablets, and smartphones.

As of February 2025, Android leads the global OS market with a 45.53% share, followed by Windows at 25.36%, and iOS at 18.25%. In the desktop segment, Windows maintains dominance with a 70.62% market share, while macOS holds 15.74%, and Linux accounts for 3.81%. The adoption of Windows 11 has been notable, capturing 31.63% of the Windows market by August 2024.

These statistics underscore the diverse landscape of operating systems and their pivotal role in shaping user experiences across various platforms.

Most software developers worldwide report the use of Windows operating system as their preferred development environment, as of 2021. Apple’s macOS was the second preferred operating system, followed by Linux. What is software development?   Software development refers to the process of creating, designing, and supporting software. It includes all the computer activities involved between the conception to the final manifestation of software. These activities are usually planned and put into stages in a logical order called the software development life cycle (SDLC). The software industry is an integral part of the IT market as the creative engine of the ever-involving market, and tech companies spend relentlessly on the development of new software products and the improvement of current ones. As such, software developers are well paid for their work. Programming languages & salary   The increasing demand for software developers is set to see their total population increase from over 20 million in 2018 to close to 29 million in 2024. Among a good deal of different programming language options, JavaScript is the most widely used programming language by software developers worldwide. Languages such as Erlang, however, which brings in the highest wages with averages of over 100,000 U.S. dollars per year as opposed to the 60,000 U.S. dollars earned on average by Java developers.

Microsoft’s Windows is the most widely used computer operating system in the world, accounting for ** percent share of the desktop, tablet, and console OS market in March 2025. Apple’s macOS ranks as the next most widely used operating system, while its iOS mobile operating system, the standard installation on all iPad devices, ranks fourth. Linux OS versions serve as the primary option for users who prefer open-source software and intend to avoid the influence of major OS developers. Operating Systems Operating systems serve as the underlying platforms which connect computer hardware and software. They provide users with the graphical interface through which they issue commands and perform tasks on electronic devices. Billions of people make use of these devices and their operating systems on a regular basis, meaning that the companies that develop these widely used technologies have a great deal of influence on the daily lives of internet users around the world. Although Microsoft Windows is the clear leader in terms of desktop operating systems, the company’s mobile device operating system failed to make a successful transition into the smartphone market, where Android and iOS are essentially the only two options.

The study investigated the views of Finnish Linux users on three operating systems: Windows, Mac OS X and Linux. The respondents' images of operating systems were charted by asking them to rate each OS on a semantic differential scale (seven-point scale between opposite adjectives, e.g. "easy to use - difficult to use", "just - unjust"). Further questions charted participation in the Linux community, and the respondents were asked whether Linux was their primary operating system, which Linux distribution they used, whether they had participated in developing Linux, on what level they had participated in the activities of the Linux community (online, local, national), and what their main reasons for using Linux were. Background variables included the respondent's gender, year of birth, type of municipality of residence, education, economic activity and occupational status, politicial party choice if parliamentary elections were held at the time of the survey, and participation in the activities of voluntary or civic organisations/associations.

According to a 2023 survey, some ** percent of software developers working with desktop environments work on Windows applications, down from ** percent of respondents in the previous year. Meanwhile, the share of software developers who used macOS for their development environment increased to ** percent in 2023, up form 39 percent in the previous year.

For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:

First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.

Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.

To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.

For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.

The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as web logs and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.

The entire dataset creation process consists of seven steps:

The packet capture was processed by the Flowmon flow exporter (https://www.flowmon.com) to obtain primary flow data containing information from TLS and HTTP protocols.

Additional statistical features were extracted using GoFlows flow exporter (https://github.com/CN-TU/go-flows).

The primary flows were filtered to remove incomplete records and network scans.

The flows from both exporters were merged together into records containing fields from both sources.

Web logs were filtered to cover the same time frame as the flow records.

Web logs were paired with the flow records based on shared properties (IP address, port, time).

The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool ua-parser (https://github.com/ua-parser/uap-python). We replaced the unstructured User-Agent string in the records with the resulting OS.

The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:

basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;

IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;

TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;

HTTP - HTTP Request Host;URL;

User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;

TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;

Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;

ICMP - ICMP TYPE;

The details of OS distribution grouped by the OS family are summarized in the table below. The Other OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.

    OS Family
    Number of flows




    Other
    42474


    Windows
    40349


    Android
    10290


    iOS
    8840


    Mac OS X
    5324


    Linux
    1589


    Ubuntu
    653


    Fedora
    88


    Chrome OS
    53


    Symbian OS
    1


    Slackware
    1


    Linux Mint
    1

Global Volume Booster Software Market was valued to grow from US$ 2.39 Billion in 2023 to US$ 6.55 Billion by 2032, CAGR of 10.6% from 2024 - 2032

The Philippines tablet market size reached 783.8 Thousand Units in 2024. Looking forward, IMARC Group expects the market to reach 1,164.7 Thousand Units by 2033, exhibiting a growth rate (CAGR) of 4.28% during 2025-2033. The market is propelled by the integration of technology into education system, the shift toward remote work and telecommuting due to the pandemic, and the increasing availability of streaming services, digital content, and online gaming platforms.

Education PC Market in terms of revenue was estimated to be worth $300 billion in 2024 and is poised to reach $600 billion by 2034, growing at a CAGR of 7.5%

Tutkimuksessa kartoitettiin suomalaisten Linux-käyttäjien mielikuvia kolmesta eri käyttöjärjestelmästä: Windows, Mac OS X ja Linux. Kysely koostuu kolmesta osasta. Ensimmäisessä osassa selvitetään vastaajan taustatietoja ja kansalaisaktiivisuutta. Toinen osa koostuu Osgood-asteikollisista käyttöjärjestelmiä koskevista 35 mielikuvamuuttujan patteristoista. Kolmannessa osassa vastaajilta kysytään tarkentavia kysymyksiä liittyen Linuxin käyttöön ja Linux-yhteisössä toimimiseen. Keskeisessä asemassa kyselyssä oli 35 vastakohtaparin kysymyspatteristo, jonka avulla selvitettiin vastaajien mielikuvia kolmesta käyttöjärjestelmän osa-alueesta: käyttökokemuksesta, eettisyydestä ja luotettavuudesta. Vastaajia on pyydetty määrittelemään käsityksiään käyttöjärjestelmistä sijoittamalla ne vastakohtien, kuten hyvä-paha välille asteikolla 1 erittäin hyvä - 7 erittäin paha. Vastakohtaparien muodostamisessa on hyödynnetty Charles Osgoodin 1950-luvulla kehittämää semanttisen differentiaalin tekniikkaa, jolla on tarkoitus mitata johonkin asiaan liittyviä konnotatiivisia merkityksiä. Lopuksi vastaajilta kysyttiin miksi he käyttävät Linuxia. Taustamuuttujina ovat mm. poliittinen aktiivisuus ja suuntautuminen, koulutus sekä ammattiasema ja onko vastaaja osallistunut Linux-järjestelmän ohjelmointiin tai muulla tavoin yhteisön toimintaan. Aineistoon liittyy haastattelut, jotka on myös arkistoitu: FSD2965 Linuxia käyttävät naiset: haastattelut 2010-2012. The study investigated the views of Finnish Linux users on three operating systems: Windows, Mac OS X and Linux. The respondents' images of operating systems were charted by asking them to rate each OS on a semantic differential scale (seven-point scale between opposite adjectives, e.g. "easy to use - difficult to use", "just - unjust"). Further questions charted participation in the Linux community, and the respondents were asked whether Linux was their primary operating system, which Linux distribution they used, whether they had participated in developing Linux, on what level they had participated in the activities of the Linux community (online, local, national), and what their main reasons for using Linux were. Background variables included the respondent's gender, year of birth, type of municipality of residence, education, economic activity and occupational status, politicial party choice if parliamentary elections were held at the time of the survey, and participation in the activities of voluntary or civic organisations/associations. Ei-todennäköisyysotanta: itsestään muotoutunut näyteNonprobability.Availability Non-probability: AvailabilityNonprobability.Availability Itsetäytettävä lomake: verkkolomakeSelfAdministeredQuestionnaire.CAWI

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

    Feature
    Data Type
    Description
    Range or Example




    IP Address
    String
    IP address belonging to the login attempt
    0.0.0.0 - 255.255.255.255


    Country
    String
    Country derived from the IP address
    US


    Region
    String
    Region derived from the IP address
    New York


    City
    String
    City derived from the IP address
    Rochester


    ASN
    Integer
    Autonomous system number derived from the IP address
    0 - 600000


    User Agent String
    String
    User agent string submitted by the client
    Mozilla/5.0 (Windows NT 10.0; Win64; ...


    OS Name and Version
    String
    Operating system name and version derived from the user agent string
    Windows 10


    Browser Name and Version
    String
    Browser name and version derived from the user agent string
    Chrome 70.0.3538


    Device Type
    String
    Device type derived from the user agent string
    (mobile, desktop, tablet, bot, unknown)1


    User ID
    Integer
    Idenfication number related to the affected user account
    [Random pseudonym]


    Login Timestamp
    Integer
    Timestamp related to the login attempt
    [64 Bit timestamp]


    Round-Trip Time (RTT) [ms]
    Integer
    Server-side measured latency between client and server
    1 - 8600000


    Login Successful
    Boolean
    True: Login was successful, False: Login failed
    (true, false)


    Is Attack IP
    Boolean
    IP address was found in known attacker data set
    (true, false)


    Is Account Takeover
    Boolean
    Login attempt was identified as account takeover by incident response team of the online service
    (true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

ASNs with values >= 500.000

IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022) Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono. ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

DVD Ripping Software Market Outlook

The global DVD Ripping Software market size is expected to witness significant growth, reaching USD 1.45 billion by 2032, from USD 0.75 billion in 2023, growing at a Compound Annual Growth Rate (CAGR) of 7.5% during the forecast period. The burgeoning demand for digital media consumption and the increasing need for media backup solutions are key factors driving this growth. The proliferation of content streaming platforms and the shift from physical media to digital formats are further accelerating market expansion.

A major growth factor in the DVD Ripping Software market is the widespread adoption of digital media. As consumers increasingly favor digital content over physical media, the demand for efficient software solutions to convert DVDs into digital formats is rising. This shift is influenced by the convenience and accessibility of digital media, which allows users to store, manage, and access their media libraries anytime and anywhere. Additionally, the increasing penetration of high-speed internet and the growing popularity of portable devices, such as smartphones and tablets, are contributing to the demand for DVD ripping software.

The commercial sector is also playing a pivotal role in propelling the DVD Ripping Software market. Businesses and educational institutions are increasingly utilizing DVD ripping software for various purposes, including the digitization of training materials, archival of educational content, and the creation of digital libraries. This trend is particularly prevalent in regions with advanced technological infrastructure and a high emphasis on digital transformation. Furthermore, the entertainment industry, which relies heavily on digital content distribution, is adopting DVD ripping software to streamline content management and distribution processes.

Technological advancements in DVD ripping software are another critical driver of market growth. Modern software solutions offer enhanced features such as faster processing speeds, improved compatibility with various file formats, and better-quality output. These advancements make the software more appealing to a broader audience, including both individuals and enterprises. Additionally, the integration of artificial intelligence and machine learning in DVD ripping software is enhancing the user experience by providing automated features that simplify the ripping process.

The regional outlook for the DVD Ripping Software market highlights North America as a leading market, driven by high digital media consumption and technological advancements. Europe is also a significant market, with increasing digital transformation initiatives across various sectors. The Asia Pacific region is expected to exhibit the highest growth rate, fueled by increasing internet penetration, rising disposable incomes, and the growing popularity of digital entertainment. Latin America and the Middle East & Africa are also witnessing a steady increase in market adoption, albeit at a slower pace compared to the more developed regions.

Operating System Analysis

The DVD Ripping Software market is segmented by operating systems into Windows, macOS, and Linux. Windows holds the largest market share, primarily due to its widespread use and compatibility with a vast array of software applications. Many DVD ripping software solutions are developed with Windows as the primary platform due to its extensive user base. The ease of use, consistent updates, and comprehensive support for various DVD formats make Windows an attractive option for both individual and commercial users.

macOS, developed by Apple Inc., is another significant segment within the DVD Ripping Software market. Although macOS has a smaller user base compared to Windows, it is popular among creative professionals and users who prioritize a seamless and integrated user experience. DVD ripping software for macOS is often designed to leverage the high-performance capabilities of Apple hardware, providing users with efficient and high-quality DVD ripping solutions. The macOS segment is expected to grow steadily, driven by the loyal and growing Apple's user base.

The Linux segment, while smaller compared to Windows and macOS, is notable for its niche user base comprising tech enthusiasts and professionals in sectors such as IT and software development. Linux users often seek open-source DVD ripp