German corporate news happens in German. An English-only pipeline reports that Europe's largest economy is quiet, and nothing in the output says otherwise.
ScrapeIt runs the collector as a managed service. You name the sections, companies and industries; we build the pipeline with German language matching including compound and umlaut variants, resolve company mentions to identifiers, and hand back CSV, JSON, Excel or a push into your warehouse.
Cadence is per section. Company and markets sections during an active story justify frequent passes; archive work runs slower.
We collect what the site renders publicly and flag subscriber restricted articles rather than working around the tier. We do not use subscriber credentials. Journalism is copyrighted and the terms restrict reuse, so the dataset is for analysis rather than republication. Bring the use case to your own counsel before the project starts.
The article record covers canonical URL, headline, standfirst, section and subsection, byline, publication timestamp, last updated timestamp, item type and the article identifier.
Company mentions are extracted and normalised to identifiers rather than left as strings. German corporate names carry legal forms, and the same company appears with and without them, with and without the group suffix, and in abbreviated forms. Matching without normalisation loses a large share of mentions and loses them unevenly between companies.
The access flag records whether the article was subscriber restricted at collection time and how much text rendered publicly, so an opening is never counted as a full article.
German text handling runs throughout: text is stored with umlauts intact, and matching handles both the umlaut and the transliterated form because URLs and slugs strip them while body text does not. Compound words are handled explicitly, since a brand name inside a German compound will not match a naive search.
Then the usual context: topic and industry tags where published, word count of the publicly rendered portion, front position from repeated observation, and the collection timestamp on every row.
Company resolution is the part that makes this source join to anything else. Once mentions carry identifiers rather than strings, coverage can be joined to financial data, to supplier lists or to a client's own account records, which is where most of the commercial value sits.
Industry tagging, where published, supports sector level questions: which industries are being written about, how that shifts, and where a client's sector sits relative to others. Published tags are reproducible in a way inferred ones are not.
Pairing with a general German title is the standard recommendation rather than an upsell. Business coverage and general coverage answer different questions, and a client running only one of them will systematically miss either the corporate detail or the political and consumer context.
Historical backfill is practical because URLs are stable and section pagination reaches back, and it runs as a one time job separate from the ongoing feed, with the access flag applied to historical rows exactly as to current ones.
Handelsblatt is Germany's leading business and financial daily. Its coverage concentrates on companies, markets, industry and economic policy, which makes it the natural counterpart to a general German news source rather than a substitute for one.
For a company with German operations, suppliers or customers, this is where corporate coverage actually appears. General news carries the big stories; the supplier dispute, the plant investment, the regulatory filing and the management change appear here first and frequently only here.
A subscriber tier sits over part of the output, with the headline, the standfirst and an opening portion rendering publicly on restricted pieces. Section fronts respond directly and a headlines feed is published and responds, so discovery is practical.
The language is German throughout, which brings the compound word and umlaut handling that any German source requires, and adds a business specific layer: German corporate naming conventions and legal forms that need normalising before company matching works.
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The failure here is the same one as on any German source, but the consequence is sharper because the content is corporate. An English language monitoring setup reports that a company received little German coverage, and the company concludes the market is quiet while a supplier dispute is being covered in detail.
Two language problems cause it. Compound nouns hide a brand or product name inside a longer word that no naive keyword search will match. Umlauts appear stripped in URLs and slugs but present in body text, so a rule written against one form misses the other.
On a business source a third problem joins them: corporate naming. German company names carry legal forms and group suffixes, appear with and without them, and are abbreviated inconsistently. Matching on a single form produces a count that is wrong per company, which is worse than one that is wrong uniformly because it makes comparisons between companies invalid.
We build the matching around all three, per company and per product, and record the surface form on every match so a client can audit the rule rather than trust it.
The fourth reason is the counterpart argument: general German news and business news are different sources covering different things, and a German monitoring setup with only one of them has a predictable hole.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, maintains the company matching as names and group structures change, and repairs the collector before your German coverage goes quiet for the wrong reason.
You see a sample first, in your format, over the companies and sections you actually track, with matched surface forms visible so you can check the rules on real German sentences.
Three ways at once, all silent. Compound nouns hide a brand name inside a longer word. Umlauts appear stripped in URLs but present in body text. And German corporate names carry legal forms and group suffixes that appear inconsistently. The result is a count that is wrong per company, which invalidates comparisons between them.
To identifiers rather than strings, with the surface form recorded on every match so you can audit the rule. That is what lets the coverage join to financial data, supplier lists or your own account records, which is where most of the commercial value in this source sits.
No. We do not use subscriber credentials. We collect the headline, standfirst, byline, section, timestamps, front position and the opening text that renders publicly, with the article flagged as restricted so an opening is never counted as a full article.
Not on its own. Business coverage and general news answer different questions, and a setup with only one will systematically miss either the corporate detail or the political and consumer context. We recommend pairing this with a general German title rather than treating either as complete.
Only as a separate labelled field where a client wants it. The German source text is never overwritten, because anything that might be quoted or challenged has to be verifiable against the original, and a translated headline sitting in the headline column is how a dataset becomes unciteable.
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.
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