Target VS Datanem
Compare Target VS Datanem and see what are their differences
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Your documents already contain a database. Datanem gets it out.
Any document with a repeating shape, whether that's dispatch notes, invoices, purchase orders, or lab results, contains tabular data that someone is still retyping by hand. Datanem reads your documents and turns them into structured tables, one row per document, with the columns you would have chosen yourself.
How it works:
Upload: Drop in up to 250 files at once (10MB each). Supports PDF, Word, plain text, or even a photo of a printed page, we can scan anything. They don't have to be digital originals.
Agree the columns: Datanem reads a sample, proposes a table, and waits for your approval. Rename a column, drop one, or add one it missed. Save the design and the next batch reuses it. Or skip the review and let it decide.
Export: Get one row per document, downloadable as Excel, CSV, or ready-to-use CREATE TABLE and INSERT statements for SQLite, Postgres, or DuckDB. Anything the model was unsure of is flagged for you to check.
What makes Datanem different:
No fixed templates! There is no rigid template to bend your paperwork into. Datanem reads what you actually have and proposes a table to match, then keeps using it so every batch lands in the same shape.
Transparency about what it leaves out! Once a table is agreed, anything outside it would normally be dropped silently; a failure you'd never find out about. Datanem lists the values it saw but couldn't file, says how many documents carried each one, and offers to add the column.
Built for GDPR from day one! Uploads are held in an EU-only bucket and never leave it. Uploads and extracted data are deleted after 30 days on the free plan. Your documents and the data drawn from them are never used to train models.
Common use cases:
Dispatch notes, delivery notes, invoices, purchase orders, remittance advice, packing lists, bills of lading, inspection reports, lab results, application forms, and timesheets.
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Datanem's answer:
Operations teams manually entering data from invoices, delivery notes, and purchase orders
Finance and accounting professionals processing remittance advice and payment records
Logistics and supply chain teams handling packing lists and dispatch notes
Quality control and lab staff working with inspection reports and test results
Anyone who receives batches of structured documents and needs them in a database or spreadsheet, fast.
Datanem's answer:
No fixed templates. Datanem adapts to your actual documents. Flags uncertain values so you check a few cells, not all. GDPR-compliant with EU storage and auto-deletion. No credit card, no demo call to start.
Datanem's answer:
Datanem is a UK-based startup founded by Alexander Green. The founding insight is simple but sharp: every organisation sits on folders full of documents with repeating structures: invoices, dispatch notes, purchase orders, lab results; someone, somewhere, is still retyping that data by hand.
The product was built around a core observation about how document processing actually fails in the real world. Most tools assume your documents fit a template. But in practice, paperwork varies. Suppliers format invoices differently, forms change over time, and scans come out crooked. Datanem's approach flips this: instead of forcing your documents into a rigid template, it reads what you actually have and proposes a table to match.
The other insight? Silent failure is the enemy. Traditional extraction tools drop anything that doesn't fit the template and never tell you. Datanem was built to surface those omissions explicitly, listing values it saw but couldn't file, showing how many documents carried each one, and offering to add the column. This transparency, letting you check a handful of flagged cells instead of all of them, is a deliberate design choice born from watching operations teams waste hours verifying data they assumed was complete.
From day one, the product was also built with GDPR in mind: EU-only storage, automatic 30-day deletion, and a strict policy of never using customer documents or extracted data for model training
Based on our record, Target seems to be more popular. It has been mentiond 261 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
// Quick check - fetch raw HTML vs rendered content // If they differ significantly, you need JS rendering Const https = require('https'); Const html = await fetch('https://target.com').then(r => r.text()); Const hasAngularVueReact = /ng-app|vue|react|__NEXT_DATA__/i.test(html); Console.log('Needs JS rendering:', hasAngularVueReact);. - Source: dev.to / 3 months ago
From camoufox import Camoufox From scrapling import Selector # Camoufox: stealth browsing with Firefox fingerprint (81MB) With Camoufox(headless=True) as browser: page = browser.new_page() page.goto('https://target.com') html = page.content() # Scrapling: adaptive parsing with CSS/XPath Sel = Selector(html) Data = sel.css('.product::text').getall(). - Source: dev.to / 3 months ago
Target → Site Map → review discovered URLs # Active endpoint discovery — find what browsing didn’t reveal Ffuf -u https://target.com/FUZZ \ -w /usr/share/seclists/Discovery/Web-Content/common.txt \ -fc 404 -mc 200,301,302,403 # JavaScript analysis — find hidden API endpoints Curl -s https://target.com | grep -oE ‘src=”[^”]+.js”‘ # Review each .js file for API paths, credentials, business logic # What to document... - Source: dev.to / 5 months ago
Import requests # Single gateway endpoint handles everything Proxy = { "http": "http://user:pass@gateway.provider.com:8080", "https": "http://user:pass@gateway.provider.com:8080" } Response = requests.get("https://target.com", proxies=proxy). - Source: dev.to / 6 months ago
From playwright.sync_api import sync_playwright # Playwright uses a real Chromium engine # TLS fingerprint matches Chrome With sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() page.goto("https://target.com") # Real Chrome TLS fingerprint. - Source: dev.to / 6 months ago
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