Web scraping and data extraction
Web Scraping Services
Turn public web pages into clean, structured datasets with a documented schema and a repeatable pipeline.
Overview
A pipeline, not a one-off script
Most scraping projects fail at the second run, not the first. A page template changes, a category is paginated differently, a field moves inside a tab, and the dataset quietly loses a third of its rows. The extraction is the easy part; keeping it correct is the work.
We build each project as a pipeline with three separate layers: fetching, parsing and validation. When a site changes, only the parsing layer needs attention, and the validation layer tells us before you notice anything in the data.
Every project ships with a field dictionary, a record count reconciliation against the source's own listings where one exists, and a written note on what could not be collected and why. If we think an official API or a licensed feed is a better answer than scraping, we say so.
Sample output
The same data, mapped
A static preview rather than an embedded map SDK, so the page stays fast. Interactive maps are built on request as part of a visualization project.
Scope
What we can extract
Anything published on a page you are permitted to access can usually be turned into a column. Common targets include:
Product and catalogue data
Names, identifiers, specifications, availability and published prices across categories.
Business directories
Company names, addresses, contact details, categories and branch listings.
Listings and inventory
Property, vehicle, job or venue listings with their published attributes and posting dates.
Reviews and ratings
Aggregate scores, review counts and, where the scope allows, review text for sentiment work.
Content and articles
Titles, authors, publication dates, tags and body text for research corpora.
Documents and tables
Values locked inside published PDFs, statistical tables and open-data portals.
Change history
Repeated collection of the same pages so you can measure what changed between runs.
Images and media references
Source URLs and metadata, collected for analysis rather than redistribution.
Output formats
Delivered the way your stack expects
- CSV
- Flat, UTF-8, one row per record. The fastest way to hand data to an analyst.
- Excel
- Workbook with data, field dictionary and run summary sheets.
- JSON / JSONL
- Nested records for anything that does not flatten cleanly, and line-delimited output for large volumes.
- Parquet
- Columnar output for large datasets loaded into a warehouse or a data lake.
- Database load
- Direct load into PostgreSQL, PostGIS, BigQuery or an S3 bucket you control.
Use cases
What clients use it for
- Pricing and assortment monitoringTrack published prices and availability across competitors on a fixed schedule.
- Lead and firmographic researchAssemble structured company records from public directories and official registers.
- Location data collectionExtract branch and store locators, then geocode them into a mappable dataset.
- Academic and policy researchCreate reproducible corpora with documented collection dates and methodology.
- Dataset backfillsRecover historical records that an API only exposes for a short retention window.
Sample dataset
What you actually receive
Sample project data. Every delivery carries a collection timestamp so you always know how fresh a row is.
| Record ID | Store name | City | Postcode | Opened | Status | Collected at |
|---|---|---|---|---|---|---|
| ST-000141 | Riverside Market | Portland | 97209 | 2018-04 | Open | 2026-08-11 |
| ST-000142 | Pearl District Market | Portland | 97210 | 2021-09 | Open | 2026-08-11 |
| ST-000143 | Alberta Street Market | Portland | 97211 | 2016-11 | Temporarily closed | 2026-08-11 |
| ST-000144 | Beaverton Market | Beaverton | 97005 | 2019-02 | Open | 2026-08-11 |
| ST-000145 | Vancouver Waterfront | Vancouver | 98660 | 2023-06 | Open | 2026-08-11 |
How it works
Five steps, every project
- Step 01
Tell us what data you need
Send the pages or sources you have in mind and the questions the data has to answer.
- Step 02
We define the data scope
We agree the field list, the coverage, the refresh cadence and the acceptable error tolerance.
- Step 03
We collect and process the data
Fetching, parsing and normalisation run as separate, monitored stages.
- Step 04
We validate the dataset
Row counts, field completeness, type checks and duplicate detection run on every batch.
- Step 05
We deliver the final result
Data lands in your chosen format or destination, with documentation and a run report.
Every extraction is quoted on its own scope: volume, source complexity and, for ongoing pipelines, run frequency and monitoring requirements. Send us the sources and the fields and you get a fixed price before anything runs.
FAQ
Questions we get asked
What kind of websites can you work with?
Publicly accessible pages, open data portals, published documents and sources you are authorised to access. Server-rendered and JavaScript-rendered pages are both fine. We do not work on sources that require defeating authentication, paywalls or anti-bot protections.
How do you handle sites that change?
Parsing is isolated from fetching, and every run is validated against expected row counts and field completeness. When a template changes, the validation step fails loudly and we repair the parser, usually within a business day on a maintained project.
Can you run collection on a schedule?
Yes. Daily, weekly and monthly schedules are all common. Scheduled projects include change logs so you can see exactly which records were added, removed or modified between runs.
How do you handle duplicates?
We agree a business key up front, for example a source identifier or a name-and-address pair, then deduplicate against it. Near-duplicates are resolved with normalised string matching and, for locations, a distance threshold. The rules are documented in the delivery.
Who owns the resulting data?
You do. We deliver the dataset and the documentation, we do not resell your project's output, and we delete working copies on request once a project closes.
What if the data is not available publicly?
Then we say so early. Depending on the case we will point you at an official API, a licensed provider, or a survey-based approach. A clear no is more useful than a dataset you cannot rely on or use.
More services
Often combined with
Street View Data
Large-scale street view imagery collection, processing and structured dataset delivery.
Learn MoreSemantic Segmentation
Pixel-level image analysis that turns imagery into counted, measured classes.
Learn MorePOI Data
Business and point-of-interest records with categories, coordinates and attributes.
Learn MoreKeep exploring
Where to go next
Related services
- Data CollectionCustom data collection and structured datasets built around one specific question.
- Google Maps DataStructured business listings with coordinates, categories, ratings and opening hours.
- POI DataBusiness and point-of-interest records with categories, coordinates and attributes.
- Geospatial DataLocation, road, boundary and land-use datasets, cleaned and projected correctly.
Related reading
- How to Collect Google Maps DataThe hard part is not getting listings. It is getting all of them, once each, with categories that mean the same thing everywhere.
- How to Build a Geospatial Data PipelineMost spatial pipelines work perfectly once. The design decisions that matter are the ones that keep them working on run twenty.
Next step
Have a specific data requirement?
Tell us the geography, the fields and the cadence you need for web scraping. You get a scoped plan, a sample and a fixed price before any work starts.