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MongoDB Retail Global

Decathlon

Application Refactoring · Real-time Analytics

Decathlon's eight-year-old pricing system was struggling to scale and process millions of daily dynamic, marketplace, and local store prices.

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And with Oneprice, it has a system ready to handle millions of data points and their related legal complexities each day across Decathlon’s 85 countries of operation.
As a result, the business’s daily data volume skyrocketed, reaching up to 1 million dynamic prices and 4 million marketplace prices.
To date, Decathlon has successfully migrated 100% of its marketplace partners and 4 million marketplace processes.
“In France alone, we have around 1 million references.

Reported impact

Business outcome

With MongoDB Atlas, Decathlon successfully migrated 100% of marketplace partners, eliminating relational bottlenecks to focus on business value.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Decathlon's eight-year-old pricing system was struggling to scale and process millions of daily dynamic, marketplace, and local store prices. Decathlon migrated to MongoDB Atlas, leveraging denormalized document structures and Online Archive to simplify data and reduce storage costs. One of these platforms is an internal pricing builder designed to centralize retail prices and maintain a single source of truth for global commerce. “The advantage of centralized pricing is that we can also centralize compliance with pricing laws, of which there are many, depending on the country,” explained Julien Lampin, Solutions Architect at Decathlon. These include local ‘subsidiarity’, which allows over 600 stores in France to adjust their own prices, as well as the launches of AI-driven dynamic pricing and a new third-party marketplace. As a result, the business’s daily data volume skyrocketed, reaching up to 1 million dynamic prices and 4 million marketplace prices.

Data flow

Operational data from legacy or distributed applications is consolidated in MongoDB Atlas Online Archive and MongoDB Atlas, where flexible document models support microservices, APIs, and downstream user experiences without batch-heavy synchronization. Upstream and downstream integrations include Kafka, AWS, APIs.

Solution architecture

2 components · 1 layer
  1. Storage
    • MongoDB Atlas Online Archive Moves colder data out of the hot cluster while keeping it available for retention, analysis, and compliance.
    • MongoDB Atlas Provides the managed document database backing Decathlon's modernized application platform.

Architecture clues

  • Cold data retention is handled with archiving to control active storage costs.
  • Kafka is used as an event or ingestion layer in the wider architecture.
  • The deployment integrates with APIs.
  • The deployment integrates with AWS.
  • The deployment integrates with Kafka.

Evidence from the source

“In France alone, we have around 1 million references. Multiply that by 600 stores, and that potentially creates 600 million timelines.”
“In France, for example, it is forbidden to sell at a loss except during sales. We are not pricing decision-makers, but if someone sets a price too low, we are obligated to raise it to be compliant.”
“Mechanically, it generated a huge load increase,”
“The advantage of centralized pricing is that we can also centralize compliance with pricing laws, of which there are many, depending on the country,”
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Decathlon customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/decathlon-modernization
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 30, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Aug 19, 2026 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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