Content:

The 2024 dataset on data developer salaries and employment attributes offers valuable insights into the evolving landscape of data developers. It includes key variables such as salary, job title, experience level, employment type, employee residence, remote work ratio, company location, and company size. This data enables detailed analysis of salary trends, employment patterns, and geographic variations in data developer roles. Researchers, analysts, and organizations can leverage this dataset to better understand compensation trends, the distribution of data developer roles across different regions, and the impact of remote work and company size on employment in this field.

Feature Description:

experience_level: Level of professional experience (e.g., junior, mid, senior).

employment_type: Type of job contract (e.g., full-time, part-time, contract).

job_title:The specific role or title of the employee (e.g., Data Engineer).

salary: The compensation received, in the original currency.

salary_currency:The currency in which the salary is paid.

salary_in_usd:The salary converted into US dollars for comparison.

employee_residence: The location where the employee resides.

remote_ratio: Percentage of work done remotely.

company_location: The geographical location of the company.

company_size: The scale of the company, often based on employee count.

Dataset Strecture:

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The developers dataset is a comprehensive collection of technical professional profiles from LinkedIn, featuring key fields such as profile ID, name, position, technical skills, programming languages, certifications, current company, work experience, education, location, and connections. This dataset is filterable and customizable, allowing you to extract specific developer roles such as Software Engineers, DevOps Engineers, Data Scientists, Full Stack Developers, and other technical professionals. Filter by job titles, skills, programming languages, location, experience level, and more to create targeted datasets. Ideal for technical recruiters, HR departments, B2D marketing teams, market researchers, and companies looking to analyze the technical talent landscape or reach developers with relevant products and services.

Synthetic AI Developer Productivity Dataset — Behavioral + Cognitive Simulation

A synthetic data generation resource for modeling behavioral and cognitive dynamics in developers.

  📘 About This Dataset

This dataset simulates productivity data from AI-assisted software developers. It blends behavioral signals, physiological inputs, and productivity metrics to explore the nuanced relationships between deep work, distractions, caffeine, AI usage, and cognitive strain.… See the full description on the dataset page: https://huggingface.co/datasets/syncora/developer-productivity-simulated-behavioral-data.

Most machine learning, data science, and artificial intelligence (AI) developers work with unstructured text data of the size between ** MB and * GB, with a combined ** percent of respondents indicating as such. Twelve percent of respondents work with unstructured video data with a size larger than * TB.

In May 2023 over 90,000 developers responded to the Stack Overflow annual survey about how they learn and level up, which tools they're using, and which ones they want.

The dataset is a collection of two CSV files and the original survey questionnaire as described below: - survey_results_schema.csv - contains 78 rows and 6 columns covering the basic schema of the survey.

• survey_results_public.csv- contains 89184 rows and 84 columns covering different questions related to users and tools they want to explore.

This dataset is good enough for exploration, and basic data analysis purposes. Alongside, Kagglers can try hands-on to solve some NLP problems as well.

Comprehensive dataset containing 29,782 verified Real estate developer businesses in United States with complete contact information, ratings, reviews, and location data.

API using Global Imagery Browse Services (GIBS) designed to deliver global, full-resolution satellite imagery to users in a highly responsive manner, enabling interactive exploration of the Earth.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

Comprehensive dataset containing 341 verified Real estate developer businesses in Wisconsin, United States with complete contact information, ratings, reviews, and location data.

Developers were asked: "Is the government data you're looking for generally available?"

This statistic shows the number of annual demand for contract vacancies of big data developers in the United Kingdom (UK) from 2008 to 2013. In 2010 the annual demand for contract vacancies of big data developers was *** jobs.

HarperDB is a distributed systems platform that combines database, caching, application, and streaming functions into a single technology. With HarperDB, you can start delivering global-scale back end services with less effort, higher performance, and lower cost than ever before. The platform is designed to focus on features that move your business forward, not fighting complex infrastructure.

HarperDB achieves any latency SLA for its users, offering unlimited node horizontal scale, up to 20,000 writes and 120,000 reads per second, and limitless global cluster capacity. By leveraging HarperDB, organizations can unlock peak performance, save millions on infrastructure costs, and accelerate their business growth. With its unified system architecture, GraphQL-style schema definition, and dynamic schema, HarperDB provides a scalable and efficient development environment for building anything better.

In this work, based on GitHub Archive project and repository mining tools, we process all available data into concise and structured format to generate GitHub developer behavior and repository evolution dataset. With the self-configurable interactive analysis tool provided by us, it will give us a macroscopic view of open source ecosystem evolution.

Developer Monetization for Vehicle Data Market Outlook

As per our latest research, the global developer monetization for vehicle data market size reached USD 4.2 billion in 2024, with a robust CAGR of 21.7% expected through the forecast period. Driven by rapid digital transformation in the automotive sector and increasing demand for connected vehicle services, the market is anticipated to reach USD 30.1 billion by 2033. The accelerating integration of advanced telematics, IoT, and data-driven business models are key factors propelling this growth, as OEMs and third-party developers alike seek innovative ways to generate revenue from the vast streams of vehicle-generated data.

The primary growth driver for the developer monetization for vehicle data market is the exponential rise in connected vehicles and the resulting surge in data generation. Modern vehicles equipped with sensors, telematics units, and infotainment systems are producing unprecedented volumes of real-time data related to vehicle health, driver behavior, location, and system diagnostics. This data is invaluable not only for OEMs but also for third-party developers, insurers, and fleet operators who can leverage it to create new services, enhance operational efficiency, and deliver personalized experiences. The proliferation of 5G networks and advancements in edge computing further enable seamless collection, processing, and monetization of vehicle data at scale, providing a fertile ground for innovative business models and revenue streams.

Another significant factor fueling market expansion is the growing ecosystem of partnerships and collaborations between automotive manufacturers, technology providers, and data aggregators. As the industry shifts from a product-centric to a service-centric paradigm, OEMs are opening up their data platforms to third-party developers through APIs and developer portals. This democratization of vehicle data access fosters a vibrant developer community capable of building value-added applications such as predictive maintenance, usage-based insurance, and advanced navigation services. The emergence of standardized data exchange protocols and regulatory frameworks around data privacy and security also play a crucial role in instilling trust and facilitating broader adoption of monetization models across the value chain.

The evolution of consumer expectations is reshaping the landscape of developer monetization for vehicle data. End-users now demand seamless, personalized, and context-aware experiences, whether it is real-time traffic updates, in-car entertainment, or proactive safety alerts. To meet these expectations, developers are increasingly leveraging AI and machine learning algorithms to extract actionable insights from raw vehicle data. This not only enhances the value proposition of connected vehicle services but also creates new monetization opportunities through targeted advertising, subscription-based offerings, and pay-per-use models. The confluence of these trends is expected to drive sustained growth and innovation in the market over the coming years.

From a regional perspective, North America currently leads the developer monetization for vehicle data market, accounting for over 38% of the global revenue in 2024. This dominance is attributed to the high penetration of connected vehicles, a mature automotive ecosystem, and a strong presence of technology giants and start-ups specializing in automotive data analytics. Europe follows closely, bolstered by stringent regulatory mandates around vehicle safety, emissions, and data privacy, as well as active initiatives promoting smart mobility and digital services. Meanwhile, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding vehicle ownership, and increasing investments in smart transportation infrastructure. These regional dynamics are expected to shape the competitive landscape and innovation trajectory of the market throughout the forecast period.

Monetization Model Analysis

Comprehensive dataset containing 750 verified Real estate developer businesses in Colorado, United States with complete contact information, ratings, reviews, and location data.

Detailed Core developers metrics and analytics for Solana, including historical data and trends.

The language learning software industry has boomed, with revenue estimated to hike at a compound annual rate of 17% over the five years through 2025-26. Its successful performance is partially due to the industry's infancy, with most language learning platforms being less than 10 years old. Language learning apps surged in popularity among consumers, providing a cheaper alternative to traditional face-to-face courses while offering greater flexibility to users concerning the time they are willing or able to dedicate to language learning. The number of start-up developers has expanded markedly, with many companies attracting funding from venture capital companies and private investors. This allowed software developers to expand their research and development budgets and make enormous strides in developing advanced language learning software technology. In 2025-26, revenue is anticipated to swell by 17.8%, to reach £172.6 million. Expansion is being supported by the widespread adoption of smartphones and an ongoing wave of technological innovation. Historically dominated by international giants like Rosetta Stone and Babbel, the industry has seen UK-based developers carve out a niche by leveraging advanced features like AI-driven adaptive learning pathways, speech recognition and real-time feedback. Over the two years through 2025-26, platforms like Busuu and Memrise have rolled out a range of innovative tools, driving heightened user engagement and improved retention. However, this heady growth masks underlying challenges, especially for smaller start-ups attempting to monetise niche solutions amid escalating operational and development costs. While the industry's high labour costs and sustained investment in research and development have kept profitability low, the average margin is likely to improve over the coming years as software sales continue to blossom domestically and abroad. Demand for language learning software, particularly from educational institutions and business clients, is set to continue booming, supporting the industry's formidable expansion. Over the five years through 2030-31, industry revenue is forecast to climb at a compound annual rate of 16.6%, reaching £372.3 million. The strategic focus will remain on rolling out improved AI-driven personalisation, adaptive learning algorithms and analytics dashboards, all aimed at boosting conversion rates and securing long-term subscription revenue. However, the rise of free, AI-powered tools like ChatGPT makes the landscape more competitive, particularly among price-conscious individual consumers. To mitigate substitution risks, UK developers will need to integrate advanced AI into their core offerings, double down on business-to-business (B2B) and regulated education contracts and address acute talent shortages in software, AI and data science roles. While profit improvement is on the horizon as platforms achieve greater scale, the industry will remain shaped by high innovation costs, stiff global competition and an ongoing battle to turn engagement into sustainable revenue.

The Developer Services Review Program is designed to meet the special needs of owners, developers, architects and contractors working on moderately- to highly-complex construction or renovation projects. Eligible projects include high-rise buildings, mercantile buildings with more than 150,000 square feet, other occupancies with more than 80,000 square feet, buildings with foundations deeper than 12 feet, and residential projects that contain more than 25 units. This metric tracks the average number of days DOB takes to process individual Developer Services Permits, grouped by the week the permit was processed. The target average process time is within 89 days. Click here for more information about DOB’s Developer Services Program.

TL;DR

Our free COVID-19 Stats and New API lets you send a web-based query to Smartable AI and get back details about global and regional coronavirus data, including latest numbers, historic values, and geo-breakdowns. It is the same API that powers our popular COVID-19 stats pages. Developers can take the returned information and display it in their own tools, apps and visualizations. Different from other coronavirus data sources that produce breaking changes from time to time, the data from our API are more stable, **detailed **and close to real-time, as we leverage AI to gather information from many credible sources. With a few clicks in our API try-it experience, developers can get it running quickly and unleash their creativity.

Click here to get started

Our Mission

“We’re not just fighting an epidemic; we’re fighting an infodemic” – WHO Director-General Tedros Adhanom Ghebreyesus

In Smartable AI, our mission is to use AI to help you be smart in this infodemic world. Information is exploded, and mis-information has impacted the decisions of governments, businesses, and citizens around the world, as well as individuals’ lives. In 2018, The World Economic Forum identified it as one of the top 10 global risks. In a recent study, the economic impact has been estimated to be upwards of 80-100 Billion Dollars. Everything we do is focused on fighting misinformation, curating quality content, putting information in order and leveraging technology to bring clean, organized information through our APIs. Everyone wins when they can make sense of the world around them.

Our Free API and Daily Data Dump

The coronavirus stats and news API offers the latest and historic COVID-19 stats and news information per country or state. The stats are refreshed every hour using credible data sources, including the country/state’s official government websites, data available on wikipedia pages, latest news reports, Johns Hopkins University CSSE 2019-nCoV Dashboard, WHO Situation Reports, CDC Situation Updates, and DXY.cn.

The API takes the location ISO code as input (e.g. US, US-MA), and returns the latest numbers (confirmed, deaths, recovered), the delta from yesterday, the full history in that location, and geo-breakdown when applicable. We offer detailed API documentation, a try-it experience, and code examples in many different programming languages.

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We upload a daily dump of the data in the csv format here.

We want it to be a collaborative effort. If you have any additional requirements for the API or observe anything wrong with the data, we welcome you to report issues in our GitHub account. The team will jump in right away. All our team members are ex-Microsoft employees, so you can trust the quality of support, I guess 🙂

Our Example Apps powered by the API

We have developed two example apps by using the API.

• COVID-19 Live Stats+News
• COVID-19 Android app

United States - Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over: Men was 1379.00000 Thous. of Persons in January of 2019, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over: Men reached a record high of 1379.00000 in January of 2019 and a record low of 523.00000 in January of 2002. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Software developers, applications and systems software occupations: 16 years and over: Men - last updated from the United States Federal Reserve on October of 2025.