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Confluent Travel and Hospitality global

Flix

Fraud Detection · Real-time Analytics

Flix needed to stop spending engineering time on self-hosted Kafka so it could move travel data in real time, reduce latency into analytical stores, and accelerate product delivery for booking, fraud, and arrival use cases.

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0.9 million rides per day
10-15% cost reduction
4-6 months down to just one month
4-6x faster

Reported impact

Business outcome

Flix delivered features faster, cut service desk demand and Kafka operating cost, improved booking and travel experiences for passengers, and created a stronger base for self-service data products and global expansion.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Flix moved its travel data platform to Confluent Cloud on AWS to modernize a self-hosted Kafka estate that had become too expensive and operationally heavy for a fast-growing travel business. The migration was executed in phases with Cluster Linking so existing systems could stay live while teams gradually moved over without downtime. Once in place, the platform used Flink for stream processing and managed connectors to push real-time data into Snowflake and Amazon S3, reducing downstream latency from batch-style 8 to 12 hour windows to near real time. Those governed streams support fraud detection, seat availability, ticket booking, arrival predictions, and a growing data mesh used by more than 50 internal teams. The same backbone also feeds AI-driven pricing and capacity systems that evaluate hundreds of millions of seat combinations and help Flix expand into new geographies faster.

Data flow

Operational travel events stream into Confluent Cloud, Cluster Linking keeps migration paths live, Flink processes governed streams, and managed outputs deliver the resulting data into Snowflake and Amazon S3 where teams use it for fraud detection, seat availability, arrival predictions, and pricing workflows.

Solution architecture

5 components · 4 layers
  1. Ingest
    • Cluster Linking Connected existing Kafka systems to Confluent Cloud during the zero-downtime migration.
  2. Compute
    • Flink Processes streams for governed, real-time travel and analytical use cases.
  3. Storage
    • Snowflake Receives real-time data exports for analytical workloads.
    • Amazon S3 Acts as a downstream storage target for near-real-time streaming data.
  4. Uncategorized
    • Confluent Cloud Runs Flix's managed streaming backbone for travel, fraud, and booking data.

Architecture clues

  • Cluster Linking enabled a phased zero-downtime migration from self-hosted Kafka.
  • Managed connectors stream data into Snowflake and Amazon S3 for a broader data mesh.
  • The platform underpins AI-driven pricing, seat availability, fraud detection, and arrival prediction use cases.

Evidence from the source

Flix now delivers new features four to six times faster - from 4-6 months down to just one month.
Our service desk requests have gone down by 80% since we moved to Confluent.
Reducing delays from 8-12 hours to near real time.
Using Cluster Linking, they connected their existing systems to Confluent Cloud and created a phased plan to move teams over gradually with clear guidelines in place to support a smooth transition.
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
    8 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. Confluent

    Flix customer story

    First-party
    https://www.confluent.io/customers/flix/
    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 Jul 22, 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 Oct 11, 2026 · Confluent · 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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