We extract Linux application metadata, Flatpak manifests, dependency trees, and release histories from Flathub. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery on your schedule.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for App Metadata objects from flathub.org. All fields typed and schema-versioned.
"app_id": "org.mozilla.firefox", "name": "Firefox", "developer_name": "Mozilla", "verified": true, "categories": "['Network', 'WebBrowser']", "license": "MPL-2.0"
| # | app_id | name | summary | developer_name | verified | categories |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Release History objects from flathub.org. All fields typed and schema-versioned.
"app_id": "org.mozilla.firefox", "version": "125.0.1", "release_date": "2024-04-16T14:32:00Z", "architecture": "x86_64", "commit_hash": "a1b2c3d4e5f6", "size_bytes": 214748364
| # | app_id | version | release_date | release_notes | architecture | commit_hash |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dependencies objects from flathub.org. All fields typed and schema-versioned.
"app_id": "org.mozilla.firefox", "runtime": "org.freedesktop.Platform/x86_64/23.08", "sdk": "org.freedesktop.Sdk/x86_64/23.08", "permissions": "['network', 'audio', 'pulseaudio']", "filesystem_access": "['xdg-download', 'xdg-run/pipewire-0']", "socket_access": "['x11', 'wayland']"
| # | app_id | runtime | sdk | permissions | extensions | dbus_access |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Statistics objects from flathub.org. All fields typed and schema-versioned.
"app_id": "org.mozilla.firefox", "total_downloads": 482910, "recent_downloads": 12450, "rating": 4.8, "review_count": 3412, "category_rank": 1, "last_updated": "2024-04-16T14:32:00Z"
| # | app_id | total_downloads | recent_downloads | rating | review_count | trending_rank |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Source & Manifest objects from flathub.org. All fields typed and schema-versioned.
"app_id": "org.mozilla.firefox", "manifest_url": "https://github.com/flathub/org.mozilla.firefox/blob/master/org.mozilla.firefox.json", "github_repo": "flathub/org.mozilla.firefox", "build_system": "flatpak-builder", "maintainers": "['mozilla-releng']", "issue_tracker": "https://bugzilla.mozilla.org/"
| # | app_id | manifest_url | github_repo | issue_tracker | translate_url | build_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Flathub scraper handles the entire Linux app ecosystem repository: metadata listings, version histories, verified publisher tracking, and runtime dependency mapping.
Extract application IDs, names, summaries, developer names, and category classifications across the entire Flathub directory.
Capture release dates, version numbers, commit hashes, and detailed release notes for every application update.
Map Flatpak sandbox permissions, filesystem access rules, DBus interfaces, and socket requirements per application.
Track applications with verified publisher badges to distinguish official releases from community maintained packages.
Extract total and recent download counts to measure application popularity and category trends over time.
Identify supported architectures including x86_64 and aarch64 for every application and runtime.
Resolve and parse underlying JSON or YAML Flatpak manifests directly from linked GitHub repositories.
Normalise application categories and subcategories to map the entire Linux desktop software ecosystem.
Run continuous pipelines at daily or weekly cadences with change detection diffing to capture new releases.
Brief in. Clean data out.
Select target categories, specific application IDs, or request a full directory extraction. We design the schema together.
We configure Scrapy crawlers, API polling logic, and manifest parsers tailored to Flathub's data structure.
Schema validation, null-rate checks, and dependency resolution testing before full pipeline launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed cadence.
Extracting structured data from Flathub requires parsing undocumented endpoints and resolving external manifests. Here is how we build resilient pipelines.
While Flathub has an API, many endpoints are undocumented and change frequently. We map and monitor these endpoints, handling pagination and rate limits automatically to extract raw JSON responses without relying solely on HTML parsing.
Critical data like build systems and granular permissions live in Flatpak manifests hosted on GitHub. Our pipeline follows these external links, parses the raw JSON or YAML manifests, and merges the data back into the primary application record.
We maintain a hash index of the latest commit and version number per application. Subsequent runs only parse and emit records when a new release is detected, reducing downstream processing load and providing a clean changelog.
Older applications often lack modern AppStream metadata fields. Our extraction logic uses fallback chains and default null handling to ensure older packages do not break the strict warehouse schema.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing manifests, and coverage drops, responding before you notice any missing data.
Researchers track the growth of the Linux desktop ecosystem, measuring application counts and publisher adoption over time.
Security teams monitor requested Flatpak permissions, identifying applications requesting excessive filesystem or socket access.
Analysts compile download trends and category popularity to understand user behaviour in the open source software space.
Software vendors track the update frequency, release notes, and user ratings of rival applications.
Platform engineers analyse runtime and SDK usage across the ecosystem to identify deprecated or vulnerable dependencies.
Machine learning teams build text corpuses of software descriptions, release notes, and technical metadata for model training.
"Flathub represents the modern Linux desktop ecosystem. Extracting its metadata provides unparalleled visibility into open source software distribution and security models."
Most teams underestimate the complexity of tracking thousands of application manifests, parsing nested dependency trees, and monitoring granular version updates. DataFlirt manages the extraction logic, handles API rate limits, and normalises inconsistent developer inputs so your engineers receive clean, structured data ready for analysis.
Everything supported by our flathub.org scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles orchestration and API polling. Playwright handles any required JavaScript rendering for complex manifest repositories.
We route requests through proxy pools to respect rate limits and prevent IP bans while polling undocumented endpoints.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About flathub.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available open source metadata from Flathub is generally permissible. DataFlirt targets only public application data, manifests, and release histories. We do not extract personal developer data or circumvent authentication walls.
We use managed proxy pools and implement strict concurrency limits to respect server load. Our request timing is modelled to prevent overwhelming the infrastructure during full directory scans.
Yes. Our pipeline resolves the GitHub repository links provided in the metadata, fetches the raw JSON or YAML manifest files, and parses the build instructions and dependencies.
We can configure pipelines to poll the directory daily or weekly. Change detection ensures that only new releases or metadata updates are processed and delivered.
Yes. We extract all requested Flatpak sandbox permissions, including filesystem access, DBus interfaces, socket requirements, and device access flags.
Yes. We provide a sample run of up to 100 applications as part of the pre-engagement scoping process so you can validate schema fit and field completeness.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one off directory dump or a continuous release monitoring feed across all applications, we scope, build, and operate the pipeline. Tell us what you need.
