Google Maps Data: Local Businesses from 1,000 US Cities Every Weekday
A weekday feed of local businesses from Google Maps for a local-marketing company: 1,000 US cities, three keywords a day, files by 10:00 UTC.
Wikipedia Scraper for Infobox, Table and Cross-Language Data
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 a month · Free project assessment · Reply within 1 business day
CSV · Excel · JSON · XML · FTP · SFTP · Amazon S3 · Google Cloud
wikipedia.orgwhat you get
Typical fields for marketplaces and directories
business_namecategoryaddressphonewebsiteratingreviews
Delivery
API, JSON feed or CSV, XLSX and JSON files
ScheduleOne-time, monthly, weekly, daily or 3 times a day
Wikipedia data scraping, managed and delivered your way
- You send
Wikipedia pages, filters, fields and a schedule
- We run the scrapers
Built, monitored and repaired by our team
- You receive the data
API, JSON feed or CSV, XLSX and JSON files
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.
Scraping Wikipedia tables and infoboxes into typed columns
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.
- Articletitle, URL, short description, lead text, section headings, first-publication and last-edit timestamps, page length, coordinates where present, main image file and interlanguage links.
- Infoboxtemplate name and every row, split into typed fields. A Founded line such as April 5, 1993, in Sunnyvale, California becomes a date and a place; Revenue US$215.9 billion (FY26) becomes amount, currency, scale and fiscal year. That is how Wikipedia company information - industry, founders, headquarters, key people, revenue, employees, website - ends up in columns you can sort.
- Tablesevery wikitable with caption, section, header rows and cells. Merged headers are unfolded, footnote markers move to their own column, flag icons become ISO country codes and thousands separators are dropped, so Wikipedia election results tables with two-row headers come out as one row per constituency and candidate.
- Categoriesvisible ones kept, maintenance ones dropped, and the category tree walked to the depth you set, with loops cut.
- Referencescited URL, title, publisher, date and identifiers such as DOI or ISBN from the citation templates.
- Revisionsrevision ID, parent ID, timestamp, size, size change, edit summary and tags - the Wikipedia edit history of the article.
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, edit history and joins to your data
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.
How Wikipedia data is organized across languages, revisions and Wikidata
- Source
- wikipedia.org
- Category
- marketplaces and directories
- Delivery
- API, JSON feed, CSV, XLSX
- Plans
- From €169 a month
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.
25Developers
500+Customers
1.5BPages extracted
15,000+Hours saved for our clients
Wikipedia scraping plans and pricing
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan detailsWhen to scrape Wikipedia for typed, cross-language data
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:
- infobox and table values as typed columns rather than wikitext or display strings;
- one entity lined up across languages, with each edition's own figures side by side;
- a slice - a category tree, a list article, a WikiProject - refreshed weekly or daily with changes flagged;
- Wikipedia rows joined to your CRM, catalog or registry by QID, ISIN or name;
- output in your schema and warehouse instead of raw markup to parse.
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ScrapeIt scopes, builds and maintains your Wikipedia dataset
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.
FAQ
Still unsure? Write to info@scrapeit.io
Do you offer a Wikipedia API for infobox and table data?
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.
Is scraping Wikipedia legal?
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.
Can you match Wikipedia articles across languages and to our own records?
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.
How do you scrape Wikipedia tables and infoboxes into flat columns?
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.
How often can a Wikipedia dataset be refreshed, and in what formats?
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.
How it works
- 1
Make a request
You share your needs, expectations, and desired timeframe. We'll suggest the best solution based on your request and budget.
- 2
We configure the crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
- 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.
- 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.
Request a quote
Tell us which sites, which fields and how often. A couple of lines is enough to start.
- We reply within 1 business day
- A sample dataset before the full run
- We sign an NDA on request
- Delivery by API, JSON feed or files