Normalized claim
Events ingested per day: 13,000,000 events/day increase
"Streams 13 million events into BigQuery per day"
GCPEvidence: Medium50/100
Moneysupermarket Group creates a real-time data platform on Google Cloud
Use case typeData platform modernizationUpdated Jul 15, 2026
Moneysupermarket Group built a new Google Cloud data platform to unify streaming and analytics data across multiple brands. The platform supports faster insights, stronger privacy controls, and a more streamlined customer and partner experience.
- Organization
- Moneysupermarket Group
- Industry
- Finance
- Location
- United Kingdom
- Published
- July 2026
Reported outcomes
13,000,000 events/day
events ingested per dayOther quantified impact
20 secondstable creation time5 secondsanalysis availability time10 secondsdata latency700 tablestables loaded through a single pipeline
Strategic outcomes
Speed & agilityEngineers can self-serve data publishing in minutesRisk & complianceImproved security and access control for PIICustomer experience & trustMore streamlined customer experience across brandsBetter decisions & insightSupports real-time insights and future sales confirmation use cases
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Table creation time: 20 seconds decrease
"Table creation in under 20 seconds"
Normalized claim
Analysis availability time: 5 seconds decrease
"Activity on the website is available for analysis within five seconds"
Normalized claim
Data latency: 10 seconds decrease
"Reduced data latency from 24 hours to 10 seconds"
Normalized claim
Tables loaded through a single pipeline: 700 tables increase
"More than 700 tables loaded through a single pipeline"
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Moneysupermarket Group
- 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
- 1Data platform modernization
- 2Real-time analytics
- 3Operational analytics
- Eliminate data silos across brands and customer channels.
- Reduce data latency and make streaming data available for analysis in near real time.
- Protect customer information while increasing the amount of data processed.
- Built an end-to-end streaming data pipeline using BigQuery, Dataflow, and Pub/Sub.
- Used serverless Google Cloud services to ingest live events, apply schema detection, and load more than 700 tables through a single pipeline.
- Applied privacy by design with IAM permissions, policy tags, and pseudonymization, with raw data locked in the base table.
- Created a single source of truth for analytics and made engineers able to self-serve data changes in minutes.
- Streams 13 million events into BigQuery per day.
- Table creation takes under 20 seconds.
- Website activity is available for analysis within five seconds.
- Reduced data latency from 24 hours to 10 seconds.
- Loaded more than 700 tables through a single pipeline.
- Expanded the analytics data estate eight times in 18 months.
Architecture
A serverless streaming analytics platform on Google Cloud using Pub/Sub, Dataflow, and BigQuery. The design includes schema detection, privacy-by-design controls with IAM, policy tags, and pseudonymization, and a single pipeline for loading hundreds of tables across multiple brands.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jul 15, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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