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MongoDB Logistics Global

Amadeus IT Group

Application Refactoring · Real-time Analytics

The shift to New Distribution Capability (NDC) bypassed traditional GDS buffers, sending massive search traffic directly to airlines’ systems.

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Furthermore, MongoDB's distributed architecture and sharding capabilities enable Amadeus to scale horizontally, effortlessly supporting the broader travel platform’s global threshold of 150,000 transactions per second.
It’s also a business that works on a massive scale, supporting over 400 airlines and handling over 2 billion boarded passengers in one year alone.
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With MongoDB, Amadeus successfully processes 20,000 searches per second with millisecond latency, while respecting airline load limits. is a major global operator in the travel industry.

Reported impact

Business outcome

With MongoDB, Amadeus successfully processes 20,000 searches per second with millisecond latency, while respecting airline load limits.

  • Customer Experience
  • Risk Reduction
  • Scalability

Solution

The shift to New Distribution Capability (NDC) bypassed traditional GDS buffers, sending massive search traffic directly to airlines’ systems. Amadeus combined MongoDB's flexible document model with machine learning and Kafka to build an intelligent cache for dynamic flight data. With MongoDB, Amadeus successfully processes 20,000 searches per second with millisecond latency, while respecting airline load limits. is a major global operator in the travel industry. It’s also a business that works on a massive scale, supporting over 400 airlines and handling over 2 billion boarded passengers in one year alone. “This generates a huge amount of traffic,” explained Ali Makke, NoSQL Expert at Amadeus. It has revolutionized retail air travel by replacing legacy systems, enabling faster product launches, and providing significantly faster, more tailored booking experiences for consumers.

Data flow

Operational events, telemetry, or transaction data are ingested into MongoDB Enterprise Advanced and MongoDB Atlas, enriched or filtered where needed, retained for fast query access, and served to analytics, monitoring, or customer-facing workflows in near real time. Upstream and downstream integrations include Kafka, CRM systems, APIs.

Solution architecture

2 components · 1 layer
  1. Storage
    • MongoDB Enterprise Advanced Stores the high-volume operational and event data that underpins Amadeus IT Group's real-time analytics platform.
    • MongoDB Atlas Stores the high-volume operational and event data that underpins Amadeus IT Group's real-time analytics platform.

Architecture clues

  • Kafka is used as an event or ingestion layer in the wider architecture.
  • The deployment integrates with APIs.
  • The deployment integrates with CRM systems.
  • The deployment integrates with Kafka.
  • The solution uses a microservices-based architecture.

Evidence from the source

“But it also involves many technical challenges, especially for airlines’ system loads and traffic.”
“NDC makes the travel booking process a lot less generic and a lot more personalized,”
“This generates a huge amount of traffic,”
“We recently passed the threshold of 150,000 transactions per second.”
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
    3 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

    Amadeus IT Group customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/amadeus-it-group
    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 Sep 25, 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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