Wikipedia keeps its facts in infoboxes and tables that differ from article to article and language to language. Our Wikipedia scraper returns them as typed columns, editions lined up.
Plans from €169/month · Free project assessment · Reply within 1 business day
We run Wikipedia data scraping as a managed job, with a handful of parallel requests and pacing built in, and we never run a Wikipedia crawler that walks every link - live requests fetch only the pages in scope. Anti-bot handling, proxy rotation and CAPTCHA solving are part of our managed service. Delivery is CSV, JSON Lines, Parquet or XLSX, to S3, BigQuery or SFTP, or through our Wikipedia scraper API, which serves the stored dataset with source URL and revision ID on every row.
We extract Wikipedia data at three levels - article, infobox and table - and stamp every row with language, page ID, revision ID and Wikidata QID, so each value traces back to the exact version it came from.
Infobox values arrive as typed fields rather than display strings, and tables with merged cells or irregular columns are parsed too, with rules written per template and language.
Wikipedia page views statistics come as daily or monthly views per article, split by access - desktop, mobile web, mobile app - and by agent, so self-declared spiders and automated traffic stay apart from readers. We also collect the 1,000 most-viewed pages per project per day, and per country, where figures come in buckets. We add monthly clickstream figures that show how readers move between articles in five languages. From these we build Wikipedia traffic statistics by category or topic, and Wikipedia statistics by language for the markets you compare.
Edit history comes as revision counts, dates, size changes, reverts and protection changes per article.
Joining is where the data earns its keep. The Wikidata item behind each article carries identifiers such as ISIN, VIAF or GeoNames, so Wikipedia rows meet your own records with far less fuzzy name matching. Names of public figures are part of the encyclopedia and stay in the rows; user pages and an editor's contribution trail are never collected as a profile of that person.
Wikipedia is not one website but a family of separately written encyclopedias, one per language, each on its own subdomain such as en.wikipedia.org or de.wikipedia.org. In September 2026 Meta-Wiki lists 348 active editions holding 68.4 million articles, 7.2 million of them in English. A Wikipedia scraper has to follow that shape. Every article sits at a /wiki/Title address, carries a numeric page ID that differs from one language to the next, gains a new revision ID with every saved edit and in most cases links to one Wikidata item: Nvidia is Q182477 in every edition that covers it.
The page is wikitext rendered into HTML. The Wikipedia infobox in the top corner is a template - Infobox company, Infobox person, Infobox settlement - whose rows are label and value pairs written for readers, not for machines. Data tables carry the wikitable class, are often sortable, and mix footnote markers, flag icons and hidden sort keys into their cells. Below the text sit the visible Wikipedia categories and a second, hidden set of maintenance categories such as CS1 maint or date-format tags. Each article also embeds a schema.org Article block with the Wikidata link, the first-publication and last-edit timestamps and the short description.
Some data never shows on the article at all: page views, the full revision history and search live on separate pages, and Chinese articles switch between simplified and traditional script by address variant.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →Wikipedia holds company facts, election results and other lists in infoboxes and tables written for readers, across 348 active editions that each keep their own figures.
Scraping pays for itself when you need:
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ScrapeIt is a managed web scraping agency. On Wikipedia we write the parsing rules per template and language, run the schedule and repair it when editors restructure an infobox or a table. Work starts with a sample: one category or list article in two languages, checked field by field against the live pages, with source URL and revision ID on every row, so a Wikipedia dataset CSV can be reused without guessing where it came from.
Yes. The ScrapeIt Wikipedia API returns article titles and URLs, typed infobox fields, table rows, categories, revisions and page views as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database. Wikipedia API pricing starts from EUR 169 a month, or EUR 199 for a one-time extract, and the project assessment is free; access is by a Wikipedia API key we issue, and Wikipedia API documentation for every field comes with the dataset.
Yes. We collect only publicly available data - everything a visitor sees on Wikipedia, from article text, infoboxes and tables to categories, revisions and page views - and we collect it legally.
Yes. Every row carries the Wikidata QID, so one entity's articles line up across languages, with each edition's own figures side by side. The Wikidata item behind each article also carries identifiers such as ISIN, VIAF or GeoNames, so Wikipedia rows join your CRM, catalog or registry with far less fuzzy name matching.
A generic Wikipedia web scraper stops at the HTML. We read rendered pages through the cached /wiki/ path, find each infobox and wikitable, and apply rules per template and language: merged headers unfolded, footnotes moved to their own column, sort keys used for numbers and dates, flags mapped to ISO codes, units and fiscal years split out. To export Wikipedia tables to Excel every week with stable columns, we keep a mapping per table and tell you when editors restructure it.
Monthly, weekly or daily for a defined slice, and close to real time when edits are followed as they happen. Revision IDs let each run ship as a full snapshot or only the pages that changed. Formats are CSV, JSON Lines, Parquet and XLSX, so a run can arrive as a Wikipedia database download of your slice. A Wikipedia dataset for LLM training usually ships as section-level text with titles; a Wikipedia dataset for RAG adds the URL and revision ID each passage needs for citation.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Wikipedia data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
