---
title: "Large-Scale Price Aggregation Pipeline – Cognilium AI"
canonical_url: "https://cognilium.ai/solutions/data-engineering-intelligence/large-scale-price-aggregator"
slug: "solutions/data-engineering-intelligence/large-scale-price-aggregator"
section: "solutions"
page_type: "pillar"
date_modified: "2026-09-29"
description: "An industrial price-intelligence pipeline: millions of product URLs a day across ~20 major retailers, queued, validated, indexed and served by API."
publisher: "Cognilium AI"
author: "Mudassir Marwat"
person_same_as:
  - "https://www.linkedin.com/in/mudassir-marwat/"
  - "https://orcid.org/0009-0008-1927-2598"
  - "https://github.com/mudassirmarwat"
  - "https://medium.com/@mudassir-marwat"
  - "https://www.youtube.com/@mudassir-marwat"
  - "https://dev.to/mudassirmarwat"
  - "https://hashnode.com/@mudassirmarwat"
  - "https://huggingface.co/mudassirmarwat"
  - "https://bsky.app/profile/mudassir-marwat.bsky.social"
  - "https://substack.com/@mudassirmarwat"
  - "https://topmate.io/mudassirmarwat"
  - "https://fueler.io/mudassirmarwat"
  - "https://www.instagram.com/mudassirmarwat/"
  - "https://www.facebook.com/mudassir.marwat"
  - "https://www.reddit.com/user/mudassirmarwat/"
  - "https://www.f6s.com/member/mudassir-marwat"
  - "https://cognilium.ai/founder"
answers:
  - "Four fields, per product, per retailer"
  - "Collect, queue, validate, index, serve"
  - "Where this runs is a budget decision, not a religion"
  - "Built with"
entities: []
related:
---

# Large-Scale Price Aggregation Pipeline – Cognilium AI

**Collect** Millions of product URLs are worked through every day across roughly twenty major retailers, with the collection tier chosen per target rather than per project.


## In short

**Collect** Millions of product URLs are worked through every day across roughly twenty major retailers, with the collection tier chosen per target rather than per project.

**Queue** Everything lands in a distributed queue with progress tracked in Redis, so the work is resumable: a crashed job restarts from its last processed record, not from the beginning.

**Validate** Schema validation, field-enforcement rules and deduplication run before anything is stored. A silently missing price is a fault to raise, never a zero to save.

**Index** Batch ingestion into Elasticsearch so the catalogue is searchable in the shape buyers actually query it, with daily price history archived to S3 alongside.

**Serve** An API over the top, so the feed is something your systems consume rather than a spreadsheet somebody downloads.

**Price** What each retailer is asking today, per product, per variant.

**Stock** Availability at the moment of collection, which is what makes a price actionable.

**Ratings** The score a product carries where it is sold.

**Reviews** The written record buyers leave, collected as text you can search.

Checking competitors by hand means your prices always lag the market.

This pipeline reads price, stock, ratings and reviews from roughly twenty major retailers on a daily cycle — millions of product URLs, queued, validated, indexed and served through an API.

It is our own product, so we can show you all of it.

fields per product: price, stock, ratings, reviews


## Four fields, per product, per retailer

A price on its own is half the picture. What a competitor charges matters differently when the item is out of stock, and reviews are where product problems surface before returns do.


## Collect, queue, validate, index, serve

The interesting engineering is not the collecting. It is what happens when a source misbehaves halfway through a run of millions of records.


## Where this runs is a budget decision, not a religion

Managed Kubernetes costs a lot, and it has its benefits.

For a business with room in the budget, managed orchestration is worth what it costs in operational calm. For a business without that room, the same containers scale horizontally on dirt-cheap bare metal and the pipeline does not care. We will tell you which one your volumes actually justify, and the answer is not always the expensive one.


## Built with

The same discipline runs underneath our other pipelines, including a 250TB Spark pipeline delivered on contract.

Bring your catalogue and your competitors

A working session on your own list: which retailers matter, how often the prices really move, and what collecting them daily would cost to run. We will also tell you when a weekly cycle would do — it usually costs a fraction.


---

Canonical HTML: https://cognilium.ai/solutions/data-engineering-intelligence/large-scale-price-aggregator