Marketing data and research
Find external data for market research, competitor analysis and content: data tools, public datasets, industry benchmarks, surveys and reports. Compare coverage, access and limitations.
Study trends in public GitHub activity across included economies at an aggregate level.
Coverage: Economies included in the dataset · Quarterly economy-level aggregates from 2020 onward
Use GH Archive to analyze public project activity and ecosystem attention over time.
Coverage: Coverage depends on the source's documented geographic and time scope · Provider-described dataset or API records available at the indicated geography and time scope.
Measure JVM library presence and release cadence, map dependency/version adoption, and identify underserved integration categories for developer tooling. Useful for founders targeting Java, Kotlin, Scala, build, observability, data-infrastructure, and enterprise developer ecosystems.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Measure JVM library presence and release cadence, map dependency/version adoption, and identify underserved integration categories for developer tooling.
Track .NET package launches, version churn, dependencies, tags, and download-based adoption signals for SDKs, data connectors, and developer tools. Strong fit for founders selling into Microsoft/.NET teams, enterprise APIs, data platforms, and internal developer portals.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Track .NET package launches, version churn, dependencies, tags, and download-based adoption signals for SDKs, data connectors, and developer tools.
Discover Ruby framework and data-library demand, compare versions, analyze dependency ecosystems, and prioritize integrations or migration tooling. Relevant to founders serving Rails/Ruby teams, data engineering libraries, API clients, and platform automation.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Discover Ruby framework and data-library demand, compare versions, analyze dependency ecosystems, and prioritize integrations or migration tooling.
Study Go module/version availability and dependency adoption, identify commonly consumed infrastructure libraries, and validate reproducible-build or supply-chain product opportunities. Particularly useful for cloud-native, infrastructure, networking, observability, and data-platform founders building for Go-heavy teams.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Study Go module/version availability and dependency adoption, identify commonly consumed infrastructure libraries, and validate reproducible-build or supply-chain product opportunities.
Quantify PHP framework/data-library demand, identify dependency clusters, monitor package/version movement, and find integration opportunities in web and API stacks. Fits founders serving PHP/Laravel/Symfony teams, API infrastructure, CMS ecosystems, and data-connected web applications.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Quantify PHP framework/data-library demand, identify dependency clusters, monitor package/version movement, and find integration opportunities in web and API stacks.
Research modern TypeScript package adoption, documentation quality, runtime compatibility, and emerging developer-tool/data-client niches outside the legacy npm registry. Good fit for TypeScript-first SDKs, edge/serverless tooling, API clients, data utilities, and founders targeting Deno and cross-runtime developers.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Research modern TypeScript package adoption, documentation quality, runtime compatibility, and emerging developer-tool/data-client niches outside the legacy npm registry.
Map installable data-science, numerical, GPU, and scientific-computing packages across channels; analyze platform/build variants and dependency availability for data products. Especially valuable for founders building data/ML tooling, scientific software, GPU workflows, notebooks, and reproducible environment products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Map installable data-science, numerical, GPU, and scientific-computing packages across channels; analyze platform/build variants and dependency availability for data products.
Map competitor categories, integrations, pricing positioning, and unmet workflow needs among Jira and Confluence teams. Useful for developer-tool founders selling collaboration, DevOps, observability, security, and data-workflow extensions to engineering teams.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Map competitor categories, integrations, pricing positioning, and unmet workflow needs among Jira and Confluence teams.
Build validation, interoperability, protocol-observability, or developer-reference data products from current protocol assignments; use registry identifiers as canonical lookup data. Strong fit for developer-tool founders needing authoritative, machine-readable protocol metadata for parsers, gateways, SDKs, and network-data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Build validation, interoperability, protocol-observability, or developer-reference data products from current protocol assignments; use registry identifiers as canonical lookup data.
Find enterprise integration, analytics, data-management, automation, and developer-platform partners; assess whether a product could reach SAP-centric data and engineering teams. Good: developer and data products that integrate with SAP platforms can use the catalog for ecosystem mapping and enterprise channel research.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Find enterprise integration, analytics, data-management, automation, and developer-platform partners; assess whether a product could reach SAP-centric data and engineering teams.
Inventory IDE extensions and developer workflow tools, inspect competitive categories and licensing patterns, and identify integration or distribution opportunities for tools targeting Eclipse-based environments. Strong for developer tooling: it exposes technical categories such as code management, database development, testing, programming languages, web services, and application development.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Inventory IDE extensions and developer workflow tools, inspect competitive categories and licensing patterns, and identify integration or distribution opportunities for tools targeting Eclipse-based environments.
Create Linux package intelligence, compatibility and dependency research, container/base-image analysis, distro coverage dashboards, or developer-facing package discovery. Strong fit for developer-tool founders working on Linux support matrices, dependency intelligence, infrastructure catalogs, SBOM workflows, or package-aware data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Create Linux package intelligence, compatibility and dependency research, container/base-image analysis, distro coverage dashboards, or developer-facing package discovery.
Discover competing extensions, language/tool ecosystem coverage, publisher activity, and potential integration niches for IDE and developer-tool products. High for IDE extensions, language tooling, code intelligence, developer workflow, and API/data products aimed at software engineers.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Discover competing extensions, language/tool ecosystem coverage, publisher activity, and potential integration niches for IDE and developer-tool products.
Estimate macOS developer-tool demand, compare CLI and infrastructure package traction, and identify installation trends for local-development products. High: Homebrew is a practical discovery and installation channel for macOS developer tools, CLIs, databases, and data infrastructure.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Estimate macOS developer-tool demand, compare CLI and infrastructure package traction, and identify installation trends for local-development products.
Discover API categories and integration surfaces, compare machine-readable specifications, and map potential SDK, monitoring, documentation, or data-integration opportunities. High: API discovery and specification data is directly useful to SaaS founders building developer integrations, SDKs, catalogs, and data products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Discover API categories and integration surfaces, compare machine-readable specifications, and map potential SDK, monitoring, documentation, or data-integration opportunities.
Research API suppliers, rapidly test external data capabilities, and distribute a developer-facing API through an established discovery and connection workflow. Strong fit for SaaS founders building developer tools or data products who need to compare external APIs or reach developers through an API marketplace.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Research API suppliers, rapidly test external data capabilities, and distribute a developer-facing API through an established discovery and connection workflow.
Measure the R ecosystem's package breadth, release activity, dependencies, and available tooling when evaluating analytics/data-product integrations or R developer demand. Strong for founders targeting R/data-science developers, analytics teams, statistical computing, and package-based integrations.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Measure the R ecosystem's package breadth, release activity, dependencies, and available tooling when evaluating analytics/data-product integrations or R developer demand.
Analyze Perl module supply, versions, documentation, dependencies, and ecosystem/search demand; power a Perl-focused catalog or developer intelligence workflow through API queries. Useful for developer-tool and data-product founders serving Perl-heavy enterprise, infrastructure, or legacy-system environments.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Analyze Perl module supply, versions, documentation, dependencies, and ecosystem/search demand; power a Perl-focused catalog or developer intelligence workflow through API queries.
Track Haskell package coverage, releases, documentation, categories, and dependency signals for language-specific infrastructure or functional-programming developer products. A focused source for founders building tools for Haskell/functional-programming teams, compilers, reliability infrastructure, or typed data services.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Track Haskell package coverage, releases, documentation, categories, and dependency signals for language-specific infrastructure or functional-programming developer products.
Build a lead and ecosystem intelligence pipeline that tracks public repositories, stars, issues, releases, and contributor activity to identify teams adopting a competing developer tool or to prioritize integrations. Directly relevant to developer-tool founders, open-source maintainers, developer-relations teams, and products whose users/projects are hosted on GitHub.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Build a lead and ecosystem intelligence pipeline that tracks public repositories, stars, issues, releases, and contributor activity to identify teams adopting a competing developer tool or to prioritize integrations.
Measure package adoption and dependency positioning for a JavaScript SDK, discover packages to support, and monitor competitor release/metadata changes before planning integrations or content. Strong fit for founders selling JavaScript/Node.js SDKs, libraries, build tools, observability agents, and dependency/security products.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Measure package adoption and dependency positioning for a JavaScript SDK, discover packages to support, and monitor competitor release/metadata changes before planning integrations or content.
Create Python ecosystem intelligence for an SDK or data product: identify fast-moving libraries, inspect supported Python versions and dependencies, and trigger release/catalog monitoring for integration opportunities. Direct fit for Python developer-tool founders, data/ML infrastructure products, SDK vendors, and package-security or dependency-management SaaS.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Create Python ecosystem intelligence for an SDK or data product: identify fast-moving libraries, inspect supported Python versions and dependencies, and trigger release/catalog monitoring for integration opportunities.
Track Rust crate releases, versions, and dependency ecosystem signals to decide whether a developer tool needs a Rust SDK, identify integration targets, or monitor competing infrastructure crates. Especially relevant to Rust-focused developer-tool founders, systems/data-infrastructure products, CI/build tooling, and security/dependency intelligence providers.
Coverage: Use the geography stated by the source; coverage can vary by dataset and release. · Track Rust crate releases, versions, and dependency ecosystem signals to decide whether a developer tool needs a Rust SDK, identify integration targets, or monitor competing infrastructure crates.
Dependency risk, OSS adoption and supply-chain product planning
Coverage: Global · Package ecosystems, releases, vulnerabilities and enterprises
API/AI adoption, innovation challenges and enterprise integration
Coverage: Global · API leaders and developers
Package discovery, dependency graphs, maintenance and ecosystem intelligence
Coverage: Global; supported package registries · Repository, package, version and dependency level
Product security intelligence, vulnerability exposure and patch prioritization
Coverage: Global products and software · CVE, CPE, vendor/product/version and severity fields
Third-party dependency risk, vulnerability monitoring and remediation
Coverage: Global open-source ecosystems · Package ecosystem, name, version or commit; vulnerability record
Developer ecosystem sizing, language trends and AI tooling demand
Coverage: Global · Developers, repositories, contributions, languages and AI projects
Vendor and dependency due diligence, security benchmarking
Coverage: Global public open-source projects · Project/repository and check-level score
Python package adoption, market sizing and version/installer analysis
Coverage: Global Python package ecosystem · Package file, timestamp, installer, Python version and region fields
Software adoption, version currency and cross-distribution competitive analysis
Coverage: Global; many OS package repositories · Repository, source package, binary package, version and status
dependency selection, package risk triage, vulnerability prevalence and ecosystem maintenance analysis
Coverage: global open-source packages across major ecosystems · package/version, vulnerability, dependency and health/security metadata
developer ecosystem discovery, code reuse/trend analysis, dependency/code navigation and AI coding context
Coverage: global public and connected organizational code hosts · repository, file, symbol, commit, language and code-host integration
AI coding-tool adoption, developer productivity and enterprise enablement
Coverage: US, Brazil, Germany and India · Developers, data scientists and designers; AI tool use
Dependency selection, ecosystem adoption and supply-chain analysis
Coverage: Global open-source ecosystems · Package version, dependency edge, advisory and project
Open-source usage, creation, interdependency and impact research
Coverage: Global open-source ecosystems · Repository, package, issue, dependency and event records
Identify needs and tooling patterns among Go developers when researching developer products or prioritizing integrations.
Coverage: Global survey; respondent reach depends on the Go community invitation · Go use, development environments, work, AI tools, challenges and sentiment
Research adoption and integration opportunities for cloud, data and developer tools.
Coverage: Global developer ecosystem; population estimates are survey-based · Cloud-native adoption, developer populations, infrastructure models, AI and data workloads
Assess developer audience needs and technology adoption using an international survey.
Coverage: 194 countries; 24,534 developer respondents · Developer roles, tools, programming languages, AI use, environments and work
Research developer preferences and identify questions for interviews or product discovery.
Coverage: International online respondent pool; coverage varies by question · Developer technologies, work, education, AI tools, compensation, platforms and attitudes
Track security requirements and exploited-vulnerability signals from an authoritative curated catalog.
Coverage: U.S. government catalog of vulnerabilities known to be exploited in the wild; affected products are global · CVE, vendor/product, vulnerability, date added, due date and action notes
Study developer ecosystem direction and technology adoption signals for developer tools and integrations.
Coverage: Global GitHub platform population; limited to activity represented on GitHub · Developer growth, repository activity, languages, AI, open source and collaboration