What challenges did PropertyGuru face before AI implementation?

Fragmented data and slow, time-consuming dashboards/reporting. Need for a single source of truth and near real-time analytics and recommendations at high traffic volumes. Uneven site performance impacting user experience.

What was the impact of the AI solution?

Reduced dashboard generation time from about 1 week to nearly instantly. Achieved 2-3 millisecond response times. Processes around 50-100 TB of BigQuery queries per day and supports 24.5 million user visits per month.

Source

GCPEvidence: Low40/100

PropertyGuru: Real-time reporting and ML recommendations with BigQuery (and Bigtable)

Use case typeRetail analytics platformUpdated Jun 13, 2026

PropertyGuru is a technology-enabled real estate marketplace in Southeast Asia that needed to address fragmented data, slow reporting, inconsistent site performance, and lack of a single source of truth. The company migrated reporting and analytics to Google BigQuery, built a near real-time reporting pipeline with Cloud Bigtable and Google Analytics-derived data, and used machine learning signals to support personalized recommendations. The architecture also uses Kubernetes, Pub/Sub, and Compute Engine to deploy, scale, and operate the platform.

Organization: PropertyGuru
Industry: Real Estate
Location: Singapore
Published: January 2018

Reported outcomes

Strategic outcomes

Better decisions & insightEstablished a single source of truthSpeed & agilityEnabled near real-time reportingNew product / capabilityAdded ML-driven recommendationsCustomer experience & trustImproved site performance consistency

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: PropertyGuru
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Story

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Real-time analytics
2Recommendation system
3Data platform modernization

Fragmented data and slow, time-consuming dashboards/reporting.
Need for a single source of truth and near real-time analytics and recommendations at high traffic volumes.
Uneven site performance impacting user experience.

Migrated analytics and reporting to BigQuery.
Combined BigQuery with Cloud Bigtable for near real-time reporting over Google Analytics data.
Used user-behavior signals from clickstream data to drive recommendation models.

Technologies

BigQuery Cloud Bigtable Google Analytics Kubernetes Pub/Sub Compute Engine Cloud AutoML

Reduced dashboard generation time from about 1 week to nearly instantly.
Achieved 2-3 millisecond response times.
Processes around 50-100 TB of BigQuery queries per day and supports 24.5 million user visits per month.

Architecture

PropertyGuru migrated analytics and reporting to BigQuery, combined BigQuery with Cloud Bigtable for near real-time reporting over Google Analytics-derived data, and used Kubernetes, Pub/Sub, and Compute Engine to deploy, scale, and operate the platform. Cloud Bigtable also supported recommendation models using behavioral signals.

Sources & evidence1

Evidence: Low40/100Evidence strength

Customer explicitly identified
Primary source available
Technical implementation details available

Type: Customer StoryPublished: Jan 1, 2018Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

PropertyGuru: Real-time reporting and ML recommendations with BigQuery (and Bigtable)

Use case focus

Challenges

Solution

Impact

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