REAL-TIME WEB SCRAPING

Data that changes fast.
Caught faster.

// THE SHORT ANSWER

Real-time web scraping captures and delivers data within minutes of it changing on the source — live prices, stock levels, flash sales, fare changes and breaking events — pushed to you via webhooks or a streaming API rather than waiting for a scheduled batch. iWeb Data Scraping runs event-driven monitoring on the entities that matter to you, so your repricer, trading model or alerting system reacts while the change is still current. It's the low-latency tier of our extraction pipeline, built for decisions that can't wait for tomorrow's file.

Get free sample in 48h See delivery specs
99%+field accuracy, QA-verified
48hfree sample turnaround
24/7pipeline monitoring
ISO 27001+ 9001 certified

Key facts

  • Latency: Minutes standard; sub-minute for top-priority entities in defined windows
  • Delivery: Webhooks · Kafka/Pub/Sub · streaming API · optional buffered batch
  • Triggers: Change-detection + rule thresholds (price, stock, listing, fare)
  • Scale: Hundreds to tens of thousands of real-time entities; burst scaling for events

Most data needs are fine on a daily or hourly batch. Some aren't. When a competitor launches a two-hour flash sale, when a fare drops, when a hot SKU sells out — a batch that arrives at midnight is a report on a decision you already lost. Real-time scraping exists for that narrow, high-value band where latency is the whole product.

We run event-driven monitoring: change-detection on your priority entities, with updates pushed the moment a threshold trips — not polled and delivered hours later. Delivery is streaming (webhooks, message queues) so your systems act programmatically. This is distinct from our RAG feeds (optimized for AI retrieval) — real-time scraping is optimized for machine-to-machine reaction: repricers, trading signals, ops alerts.

WHAT'S INCLUDED

Built for the minutes
that matter.

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Change-detection monitoring

We watch your priority entities continuously and fire only when something actually changes — signal, not noise.

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Minutes-level latency

From source change to your endpoint in minutes, with tighter windows for the highest-priority entities.

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Streaming delivery

Webhooks, message queues (Kafka, Pub/Sub) and streaming API — your systems react programmatically, no polling.

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Threshold alerts

Fire on rules that matter: price drops >X%, back-in-stock, new competitor listing, fare below target.

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Priority tiering

Not everything needs to be real-time — tier entities so budget concentrates where latency pays off.

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Burst-ready for events

Campaign windows, launches and sale days scale up refresh automatically, then settle back.

SEE THE DATA FIRST

What you'll actually receive.

Real sample structure from this feed. Your free 48-hour sample comes in your category, in this shape — CSV, JSON or straight to your warehouse.

Change-event stream — pushed within minutes of the source changing.
event_time product platform event old_value new_value
18:02:14 iPhone 15 128GB Amazon price_drop 69999 64999
18:05:41 iPhone 15 128GB Flipkart back_in_stock out in
18:11:09 Galaxy S24 Amazon promo_start — Bank offer 10%
18:19:52 OnePlus 12 Flipkart price_drop 61999 59999
↑ Sample structure — illustrative values. Your data reflects your platforms and category. Get this for your data →
DELIVERY SPECS

The details procurement asks for.

LATENCY Minutes standard; sub-minute for top-priority entities in defined windows
DELIVERY Webhooks · Kafka/Pub/Sub · streaming API · optional buffered batch
TRIGGERS Change-detection + rule thresholds (price, stock, listing, fare)
SCALE Hundreds to tens of thousands of real-time entities; burst scaling for events
TIMELINE Sample stream in 48h · production integration ~1–2 weeks
PRICING By entities × latency tier × frequency; real-time is priced above batch by design
COMPLIANCE Public data only · rate-limited collection · ISO 9001/27001 · NDA-first

Every engagement is NDA-first and starts with a free sample — judge the data before any commitment.

THE HONEST COMPARISON

Why a data layer, not a DIY script or a rigid tool

In-house DIY Generic SaaS tool iWeb Data Scraping
Setup & maintenance You build scrapers, fight anti-bot, fix breakages weekly Rigid templates, breaks on site changes, slow support Fully managed — we build, monitor and fix, you never touch a proxy
Data quality Best-effort, no QA layer, silent failures Generic parsers, frequent gaps 99%+ field accuracy, QA-verified, monitored 24/7
Coverage Limited to what you can maintain Only supported sites Any public site or app, at scale
Compliance Your legal risk to manage alone Often opaque about methods ISO-certified, PII-scrubbed, NDA-first, documented
Time to value Weeks to months of engineering Fast but inflexible Free sample in 48h, production in days
WHO USES THIS

Built for the person
who owns the number.

PRICING ENGINE

React while it's current

Competitor price drops stream into your repricer in minutes — powers the same use case as our price intelligence solution, at low latency.

TRADING / ALT-DATA

Time-sensitive signals

Flash-sale detection and stock-out events as they happen, for models where minutes of edge matter — see alternative data.

OPS & MEDIA

Alert, don't report

Back-in-stock, new listing, availability drop — pushed to Slack or your system the moment it trips.

RELATED:AI Agent & RAG Feeds →Managed Web Scraping →Price Intelligence →
FAQ

Before the first call.

For us, real-time means minutes between a change on the source and the update arriving at your endpoint — with sub-minute latency achievable for top-priority entities in defined windows. It's honest about physics: we detect and push on change rather than claiming instantaneous, which no external scraper can truthfully promise.

Optimization target. RAG feeds are structured for AI retrieval — chunked, embedding-friendly, queried at answer time. Real-time scraping is structured for machine reaction — streamed to repricers, trading models and alerting via webhooks and queues. Same low-latency core; different last mile for a different consumer.

No — and you shouldn't. Real-time costs more than batch by design, so we tier entities: the SKUs, fares or names where minutes of latency pay off run real-time, the rest run on hourly or daily batch. Concentrating latency budget where it matters keeps the program efficient.

Yes — refresh scales up automatically during defined event windows (flash sales, launches, rebalance days) and settles back afterward, so you get burst freshness when it counts without paying peak rates around the clock.

Get a free sample dataset in 48 hours.