We extract company stacks, tool profiles, category rankings, and developer decisions from StackShare. Delivered as clean JSON, CSV, or Parquet to your data lake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Company Stacks objects from stackshare.io. All fields typed and schema-versioned.
"company_name": "Airbnb", "industry": "Travel", "employee_count": "10000+", "application_and_data": "['React', 'Java', 'Ruby']", "devops": "['Docker', 'Kubernetes']", "verified_stack": true, "followers": 1420
| # | company_name | website | industry | employee_count | location | application_and_data |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tool Profiles objects from stackshare.io. All fields typed and schema-versioned.
"tool_name": "PostgreSQL", "category": "Databases", "github_url": "github.com/postgres/postgres", "stars": 12400, "upvotes": 4820, "stack_count": 145921, "alternative_to": "['MySQL', 'MongoDB']"
| # | tool_name | category | description | website | github_url | stars |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Stack Decisions objects from stackshare.io. All fields typed and schema-versioned.
"tool_chosen": "Next.js", "tool_rejected": "Gatsby", "author_role": "Frontend Engineer", "company": "Vercel", "date_posted": "2023-11-12", "rationale": "Better SSR performance and API routes.", "upvotes": 342
| # | decision_id | tool_chosen | tool_rejected | author_name | author_role | company |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories objects from stackshare.io. All fields typed and schema-versioned.
"category_name": "Frontend Frameworks", "total_tools": 142, "top_tool": "React", "top_tool_stacks": 210432, "trending_tool": "Svelte", "trending_growth_pct": 42.5, "related_categories": "['Static Site Generators']"
| # | category_name | total_tools | top_tool | top_tool_stacks | trending_tool | trending_growth_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Developer Profiles objects from stackshare.io. All fields typed and schema-versioned.
"username": "johndoe", "role": "Senior Backend Engineer", "company": "Stripe", "github_handle": "johndoe_dev", "tools_used": "['Go', 'Redis', 'Kafka']", "decisions_written": 12, "followers": 89
| # | username | display_name | role | company | location | github_handle |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our StackShare scraper maps the relationships between companies, developers, and the tools they adopt. We handle GraphQL interception, pagination, and schema normalisation automatically.
Extract full application, utility, devops, and business tool categorisation per company profile.
Capture upvotes, stack inclusion counts, and GitHub metric overlays for any developer tool.
Scrape developer rationale for choosing or migrating away from specific tools.
Monitor tool rankings and market share shifts within specific software categories.
Extract StackShare Alternative to graphs for competitor analysis and positioning.
Pull GitHub stars, forks, and license data linked to StackShare tool profiles.
Filter and flag stacks officially verified by company employees versus community submissions.
Map relationships between developers, their endorsed tools, and their employers.
Identify when companies add or remove tools from their public stacks over time.
Push new stack decisions directly to internal dashboards in real time.
Brief in. Clean data out.
Provide target categories, tool names, or company lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for stackshare.io.
Schema validation, null-rate checks, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
StackShare relies on dynamic loading and GraphQL endpoints. Here is how we extract clean data without triggering rate limits.
Instead of parsing DOM elements, we intercept and decode StackShare internal Apollo GraphQL responses for structured data.
Company lists and decision feeds require stateful pagination. We manage cursor tokens automatically across thousands of pages.
We distribute requests across residential and datacenter proxy pools to avoid 429 Too Many Requests errors during deep crawls.
StackShare frequently updates its category taxonomies. We map legacy categories to your internal taxonomy to ensure consistency.
We track last updated timestamps to only scrape profiles that modified their stack since the last run, reducing compute costs.
Sales teams target companies based on their current tech stack and recent tool adoptions.
Product managers track alternative-to graphs and market share shifts against rival tools.
Identify when a target account starts evaluating or implementing competing software.
Analyse decision rationales to refine messaging and identify key features developers care about.
VCs monitor trending open-source tools and enterprise adoption rates to spot breakout startups.
Analyse which tools are frequently used together to prioritise integration partnerships.
"StackShare holds the most accurate map of B2B software adoption. Extracting it turns qualitative developer preferences into quantitative market intelligence."
Building a reliable scraper for StackShare requires reverse-engineering their GraphQL API, managing pagination cursors, and maintaining proxy health. DataFlirt handles the extraction infrastructure so your revenue and product teams can focus on actionable signals, not pipeline maintenance.
Everything supported by our stackshare.io scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass brittle HTML parsing by directly querying the backend APIs powering the frontend application.
We maintain diverse IP pools to distribute load and prevent IP bans during high-volume category crawls.
Pipelines run on Kubernetes with Airflow handling dependency management and failure retries.
Data delivered to where your team already works — no new tooling required.
About stackshare.io scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible tech stacks and tool profiles is generally permissible. DataFlirt only extracts public data and does not bypass authentication to access private profiles.
We intercept the underlying GraphQL requests rather than attempting to render and parse the DOM, ensuring higher reliability and faster extraction.
Yes. We can iterate through a tool profile and paginate through all public companies that list it in their stack.
Yes. We capture the full text of decisions, including the tool chosen, the tool rejected, and the author stated rationale.
We can configure pipelines to run daily, weekly, or monthly depending on your requirements for tracking stack changes.
We extract StackShare native category structure. You can apply mapping logic downstream, or we can build custom transformation steps into your pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full dump of developer tools or continuous monitoring of competitor tech stacks, we build the pipeline. Tell us your requirements.
