Real‑Time Web Scraping Services

Data that moves at the speed of the web

When seconds make the difference between a winning trade and a missed opportunity, batch processing won't cut it. Our real‑time pipelines extract, validate, and stream web data to your systems within milliseconds — continuously, not on a schedule.

Streaming infrastructure trusted by
< 500msEnd‑to‑end latency
MillionsEvents / minute
99.99%Stream uptime
stream-config.yaml
# Continuous streaming pipeline
source: "wss://live-prices.example.com"
render: false
delivery:
  type: "kafka"
  topic: "raw.prices"
latency_sla_ms: 500
compression: "snappy"
Live stream active
8,400 events/min
Last event
0.2 seconds ago

Trusted data from leading platforms

Expedia Shopee Tripadvisor Amazon Flipkart Swiggy Zepto Blinkit Booking Airbnb MakeMyTrip Expedia Shopee Tripadvisor Amazon Flipkart Swiggy Zepto Blinkit Booking Airbnb MakeMyTrip
Overview

When fresh means now, not “this morning”

Real‑time web scraping continuously monitors and extracts data from target websites, delivering structured payloads to your streaming infrastructure within milliseconds of a change. Instead of polling on a timer, the pipeline listens for updates — price changes, breaking news, inventory shifts — and pushes them to you the moment they happen.

Built on the same distributed engine as our batch services, but tuned for sub‑second latency, exactly‑once semantics, and seamless integration with Kafka, Kinesis, WebSockets, or any HTTP endpoint you already use.

  • End‑to‑end latency under 500ms, tuned to your SLA
  • Push delivery to Kafka, Kinesis, WebSocket, or webhooks
  • Event‑driven: trigger scrapes via API, webhook, or message queue
  • Auto‑scaling to handle spikes from 1 to millions of events/min
  • Exactly‑once delivery guarantees for transactional pipelines
Business challenges

Why batch windows cost more than you think

In industries where data value halves every few minutes, these are the bottlenecks that real‑time pipelines eliminate.

01

Stale data drives bad decisions

A price change that happened 15 minutes ago might as well not exist for an algorithmic trader. Real‑time pipelines close the gap to sub‑second freshness.

02

Polling overhead wastes resources

Scraping every 5 minutes when nothing changes still burns compute and proxy credits. Event‑driven extraction only costs you when there's new data.

03

Batch integration breaks real‑time consumers

Your downstream systems expect a continuous stream, not a file drop every hour. Real‑time pipelines natively speak Kafka, Kinesis, and WebSocket — no batch adapter needed.

Our solution

A streaming pipeline that never sleeps

Everything below runs on the same infrastructure that delivers under 500ms latency at millions of events per minute.

Sub‑second extraction engine

Headless browsers and lightweight HTTP clients are selected per target to minimise overhead. Warm pools eliminate cold starts; connection reuse cuts TLS handshake time.

Native streaming delivery

Push events directly into your Kafka topic, Kinesis stream, or WebSocket endpoint. Payloads arrive as JSON, Avro, or Protobuf — exactly as your consumers expect.

Event‑driven triggers

Scrapes can fire in response to webhooks, API calls, or messages on a queue — not just on a timer. Ideal for reactive architectures and just‑in‑time data collection.

Live latency dashboard

Monitor end‑to‑end latency, event throughput, and error rates in real time. Set alerts on latency breaches and never wonder if the stream has stalled.

low-latency-pipeline.json
// Optimised for sub‑500ms delivery
{
  "selector": "css:div.price",
  "transport": "kafka",
  "topic": "live.prices",
  "compression": "lz4",
  "latency_sla_ms": 500
}
Process

From target definition to live data stream

1

Latency profiling

We benchmark your target sites to determine the optimal extraction method and the realistic latency floor — shared before any code is written.

2

Stream configuration

We set up the pipeline's output — Kafka topic, Kinesis stream, or webhook — and agree on payload schema, compression, and delivery semantics.

3

Shadow run & validation

The pipeline runs in parallel with your existing data source for a few days. We compare latency, completeness, and schema accuracy until you're confident.

4

Cutover to live

When the numbers match, you switch your consumers to the new stream. We monitor 24/7 and handle scaling, retries, and target changes.

What you receive

Deliverables at each stage of real‑time onboarding

1
Day 1

Latency profile & architecture doc

A written document detailing the optimal extraction method per target, expected end‑to‑end latency, and the delivery architecture — reviewed with your engineering team.

2
Week 1

Shadow stream with comparison dashboard

A live, parallel stream delivering events to a test endpoint. A dashboard compares latency and completeness against your current source.

3
Week 2

Production cutover & runbook

Live stream cutover with a written operational runbook covering monitoring, alerting, and escalation paths — owned by our team, shared with yours.

Ongoing

24/7 stream health monitoring & monthly latency report

Continuous monitoring of latency percentiles, throughput, and target‑site changes. Monthly summary shared with your team.

<500ms
Median end‑to‑end latency
12M+
Events streamed per minute (peak)
99.99%
Stream uptime SLA
across all real‑time pipelines
4ms
Average parse time per event
Who needs real‑time

Use cases where seconds matter

📈

Financial Trading

Live price feeds from public exchanges and alternative data sources, streamed into trading engines within milliseconds.

📰

Breaking News

Monitor news sites, blogs, and press releases in real time — push alerts the moment a headline matches your keyword.

🛒

Dynamic Pricing

Stream competitor price changes into your repricing engine continuously, not every hour. Adjust your prices while the window is still open.

🔔

Operational Alerting

Watch supplier portals for stock changes, shipping updates, or compliance notices — and trigger internal workflows the moment they appear.

Why real‑time beats batching

Real‑time web scraping vs. batch alternatives

Capability Real‑Time Web Scraping Scheduled (Hourly) Self‑Serve API (Polling)
Data freshnessSub‑secondUp to 60 minutes staleAs fast as you poll
Push delivery
Native streaming output (Kafka, Kinesis)
Event‑driven triggers
Exactly‑once delivery±
Resource efficiency (no idle polling)±
What real‑time users say

“Our latency dropped from 15 minutes to under 300ms”

★★★★★

"Switching from a cron‑based scraper to ScraperScoop’s real‑time pipeline was the single biggest performance improvement our trading desk has seen. Data arrives faster than we can process it — and that's exactly what we needed."

VT
Head of Quantitative ResearchVelocity Trading
★★★★★

"We built a live news alert product on top of their streaming pipeline. The latency is so low our users see news before it hits the aggregators. It's become our competitive edge."

FP
CTOFlashPulse News
★★★★★

"The fact that we could just connect our existing Kafka cluster and start receiving events the same day was incredible. No adapters, no middleware — just a clean stream of validated JSON."

RS
Data Platform LeadRetailStream Analytics
Integrations

Stream directly into your existing event infrastructure

Zero middleware. Just clean, typed events delivered exactly where your consumers expect them.

🔷
Apache Kafka
📡
Amazon Kinesis
🔗
Webhooks (HTTP)
🔌
WebSocket
📦
Google Pub/Sub
📄
JSON / Avro / Protobuf

See how fast your data can move

Share a target URL and we'll return a latency benchmark — usually within one business day.

Frequently asked

Questions about real‑time scraping

Real‑time web scraping continuously extracts data from target sites and streams it to your systems within milliseconds — rather than running on a fixed schedule. It's built for use cases where freshness is measured in seconds, not hours.
Typical end‑to‑end latency is under 500ms from source change to delivery. For high‑frequency targets, we can tune the pipeline to deliver within 100ms. Latency depends on target site response times and geographic routing, both of which we optimise.
Our streaming infrastructure scales horizontally based on event rate. It can process millions of events per minute, with back‑pressure handling and exactly‑once delivery semantics when pushing to Kafka, Kinesis, or similar platforms.
Yes. Pipelines can be triggered by webhooks, API calls, or messages on a queue. This allows event‑driven scraping — for example, extract a product page the moment a new SKU appears in your inventory system.
We deliver directly to Kafka topics, Amazon Kinesis streams, WebSocket endpoints, or HTTP webhooks. Payloads can be structured as JSON, Avro, or Protocol Buffers — matched to your downstream consumer.

Let's measure the latency of your target together

Send us a URL and we'll benchmark how fast we can extract and deliver data from it — no commitment, no sales call. If the numbers work for you, we'll have a streaming pipeline live within two weeks.

Most pipelines move from benchmark to production in under 10 business days.

99.99% stream uptime Exactly‑once delivery guarantees Dedicated streaming engineer
From the blog

Insights on retail pricing strategy

🚀 Start Your Data Project

Get a Free Data Sample

See exactly what our data looks like before you commit. No credit card required, no spam — unsubscribe anytime.

  • Custom sample matching your target cuisines and cities
  • Full JSON/CSV export of live menus
  • Dedicated food data expert walkthrough
  • POC turnaround within 24 hours

Start Extracting Data Today

Tell us your requirements and get a custom quote within 2 Working Hours.