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Google Cloud Travel and Hospitality Indonesia

tiket.com

Real-time Analytics

tiket.com needed a more durable and scalable compute and data platform to support millions of daily travel transactions, large traffic spikes, and its push to become a more data-driven and AI-enabled online travel company.

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Enables near real-time data analysis for 50M+ users with BigQuery
Scales to handle 1M+ concurrent users using Google Kubernetes Engine
serves over 50 million customers

Reported impact

Business outcome

tiket.com now supports over 50 million customers, handles more than one million concurrent users, and uses Google Cloud analytics and Gemini workflows to improve efficiency while serving millions of daily transactions.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

tiket.com consolidated its online travel platform on Google Cloud so booking traffic, analytics, and AI-powered workflows could scale together. Google Kubernetes Engine and Compute Engine provide the resilient compute layer that supports flights, hotels, attractions, and event-ticket bookings, including traffic spikes that exceed one million concurrent users. BigQuery enables near-real-time analysis over the behavior of more than 50 million customers, while Cloud Spanner stores large volumes of operational data used by the company's Data and AI teams for statistical and machine learning models. Gemini models are being integrated into daily workflows and custom agentic chatbots to improve internal productivity and customer-support capabilities. By bringing compute, analytics, and AI together on one platform, tiket.com can keep serving heavy transaction volumes while improving how it responds to market shifts and customer demand.

Data flow

Travel-booking traffic flows through GKE and Compute Engine, operational data is stored in Cloud Spanner, customer and transaction behavior is analyzed in BigQuery, and insights plus internal assistance are surfaced through Gemini-powered workflows and chatbots.

Solution architecture

5 components · 2 layers
  1. Compute
    • Google Kubernetes Engine (GKE) Handles containerized booking workloads and high-volume concurrent user traffic.
    • Compute Engine Provides the durable compute foundation for tiket.com's core platform.
    • Gemini Improves internal efficiency and powers custom agentic chatbot workflows.
  2. Storage
    • BigQuery Supports near-real-time analytics over customer and transaction activity.
    • Cloud Spanner Stores operational data used for large-scale analysis and modeling.

Architecture clues

  • BigQuery is used for near-real-time analysis while Cloud Spanner stores operational data.
  • Gemini is being embedded in daily workflows and custom agentic chatbots.
  • The platform must accommodate extreme spikes such as exclusive concert-ticket demand.
  • tiket.com migrated to a fully cloud-based infrastructure using GKE and Compute Engine.

Evidence from the source

By migrating to a fully cloud-based infrastructure utilizing Google Kubernetes Engine (GKE) and Compute Engine, tiket.com achieved seamless scalability.
Cloud Spanner to securely store and analyze vast amounts of operational data.
Enables near real-time data analysis for 50M+ users with BigQuery
Scales to handle 1M+ concurrent users using Google Kubernetes Engine
integrating Gemini models into daily workflows and building custom agentic chatbots
Transparency Evidence & methodology 75/100 reference value · 77% 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 value75/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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
    5 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 specificity8/10
    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. Google Cloud

    tiket.com customer story

    First-party
    https://cloud.google.com/customers/tiket
    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

  • Sitemap last modified Sep 17, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 17, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    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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