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MongoDB Travel and Hospitality Europe

FAIRTIQ

Real-time Analytics

FAIRTIQ needed a scalable, high-availability backend to process 320k+ trips/day across six countries, ensure near-zero downtime, support rapid growth, and handle noisy GPS-based data accounting.

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Currently FAIRTIQ processes 320,000 trips daily and is available in eight European countries where it’s used actively by over 1.3 million people.
This meant the employee’s aggregation query, which writes large intermediate results to disk on the secondary node, was effectively executed 300 times.

Reported impact

Business outcome

320k+ daily transactions with 99.99% uptime Accurate auto ticketing from GPS data Architecture flexible enough to expand into 6 countries

  • Customer Experience
  • Risk Reduction
  • Scalability

Solution

FAIRTIQ needed a scalable, high-availability backend to process 320k+ trips/day across six countries, ensure near-zero downtime, support rapid growth, and handle noisy GPS-based data accounting. Built on MongoDB Atlas, FAIRTIQ leverages a cloud-native, microservices-ready platform offering flexible performance, high uptime (99.99%), and rapid deployment. Users simply check in via the FAIRTIQ app at the start of their journey. Check-in triggers sensor data collection, the app detects when the trip ends, and a journey mapping algorithm reconstructs the route using timetables, graph algorithms, and neural networks. Currently FAIRTIQ processes 320,000 trips daily and is available in eight European countries where it’s used actively by over 1.3 million people. Now expanding into new markets, the company’s goal is to simplify sustainable mobility internationally.

Data flow

Operational events, telemetry, or transaction data are ingested into MongoDB Atlas Data Federation 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 AWS, ticketing systems, APIs.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the high-volume operational and event data that underpins FAIRTIQ's real-time analytics platform.
  2. Serving
    • MongoDB Atlas Data Federation Lets FAIRTIQ query operational and archived data together from one interface.

Architecture clues

  • The deployment integrates with APIs.
  • The deployment integrates with AWS.
  • The deployment integrates with ticketing systems.
  • The platform is operated for very high availability.
  • The solution uses a microservices-based architecture.

Evidence from the source

“A big advantage is if you release the schema upgrade, you release the microservice – you don’t need a database migration script, which will complicate your release pipeline. That’s important if you have continuous delivery, so it was the right thing for us.”
“As a startup, you want to be fast,”
“The way I see it is that the schema sits inside the application as opposed to inside the database,”
“We had only four months to bring our journey mapper from a prototype to a full product with pricing payment, customer care service, and integrate it with public transport systems.”
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 impact15/25
    1 metric 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

    FAIRTIQ customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/fairtiq
    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 May 23, 2025 · 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 Dec 12, 2025 · 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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