A practical checklist for turning a website, marketplace, directory, or document source into a clear data request.
Start with the business question
A good scraping project starts before anyone writes code. The first step is to write down the decision the dataset should support. Are you trying to find new leads, monitor competitor pricing, enrich a CRM, track real estate supply, summarize news, or build a research database? That answer changes the source selection, fields, cadence, quality checks, and delivery format.
When clients send The Scrape Lab a target URL, I usually ask for the operational question behind it. A lead generation scrape needs clean company names, contact fields, categories, locations, and source URLs. A price monitor needs timestamps and repeated runs. A research dataset may need more source context, document metadata, and notes about missing fields.
- Write one sentence that explains what the data will help you decide.
- Name the person or workflow that will use the output.
- Decide whether the goal is exploration, one-time collection, or recurring monitoring.
Share source examples, not just a homepage
A homepage rarely shows the real scraping problem. The useful examples are search result pages, category pages, detail pages, profile pages, listing pages, PDF links, API-looking endpoints, and any filters that matter. If a website has different templates for different countries, categories, or record types, include examples of each one.
This is also where feasibility checks happen. A scoping review should look at whether the content is public, whether the fields appear in the HTML or require browser rendering, whether pagination is stable, what robots.txt says about crawler traffic, and whether the site returns normal HTTP status codes for the pages you care about.
- Send 5 to 10 representative URLs.
- Include the filters, search terms, locations, or categories you expect to use.
- Mark which fields are visible on list pages and which require opening detail pages.
Define fields like a final spreadsheet
Field names should be decided early. If the final output needs columns like business_name, website, phone, city, rating, review_count, price, currency, availability, published_at, source_url, or scraped_at, list them before the build. This prevents a common failure mode: the scraper technically works, but the output is too messy for a spreadsheet, database, dashboard, or CRM import.
I also recommend tagging every field as required, optional, or derived. Required fields block delivery if they are missing. Optional fields are collected when available. Derived fields are created during cleanup, such as normalized dates, parsed currencies, deduplication keys, or category mappings.
- Required: fields that must be present for the row to be useful.
- Optional: fields that improve the dataset but may not appear for every record.
- Derived: cleaned, normalized, or calculated fields produced after scraping.
Choose delivery and maintenance before the run
Delivery is part of the project, not an afterthought. Apify datasets can be exported in common formats such as JSON, CSV, XML, Excel, HTML table, RSS, and JSONL, but many teams need more than a download. A useful workflow might push clean records to Google Sheets, a Postgres table, Airtable, Notion, Slack, a dashboard, or an internal API.
Cadence is equally important. A one-time dataset can be reviewed manually after collection. A recurring scrape needs scheduling, retry behavior, alerting, quality thresholds, and a plan for layout changes. That is why The Scrape Lab separates one-time scraping setup from managed data workflows.
- One-time export: best for research, audits, enrichment, and initial datasets.
- Recurring workflow: best for price tracking, listings, sales leads, news, and compliance monitoring.
- Managed pipeline: best when the data needs cleanup, validation, and delivery every run.
Need this handled for your source?
If you already have a source in mind, start with a Data Investigation. I will review the target, identify the fields that are realistically collectable, and recommend the simplest path before you pay for a full scrape.
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