Normalized claim
Annual data integration cost reduction: 80% decrease
cut CMAI annual data integration costs by ~80 percent
Transport for NSW cuts data integration costs by ~80% using AWS Glue (public transportation analytics modernization)
Use case typeData governanceUpdated Jun 13, 2026
Transport for NSW (TfNSW), Australia’s largest public transportation agency, modernized its analytics environment to support over 2 million passengers per day across rail, metro, light rail, ferry, bus, and road services. The agency replaced a legacy third-party ETL platform that managed 400+ ingestion workflows and had become costly to operate, complex to scale, and dependent on specialized expertise. The modernization created a more resilient, scalable data ecosystem for mobility analytics and planning across New South Wales.
- Organization
- Transport for NSW
- Industry
- Public Sector
- Location
- Australia
- Published
- May 2026
Reported outcomes
3 months to days
build and deploy time for new ingestion jobsTime & speed
−80%annual data integration cost reduction
Strategic outcomes
Cost efficiencyReduced data integration costsSpeed & agilityAccelerated ingestion job deliveryEmployee experienceReduced dependence on specialistsBetter decisions & insightImproved stakeholder visibility and insights
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Build and deploy time for new ingestion jobs: 3 months to days decrease
reducing build times from several months to a few days
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Transport for NSW
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Case Study
Reduce operational risk caused by concentrated specialist knowledge and dependency on external support
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Data Integration
- 2Analytics Modernization
- 3Workflow Orchestration
- Modernize a costly third-party ETL environment supporting 400+ ingestion workflows.
- Reduce operational risk caused by concentrated specialist knowledge and dependency on external support.
- Improve development speed and resilience for transport analytics and planning.
- Migrated ETL workloads to AWS Glue, a serverless data integration service.
- Used AWS Step Functions for orchestration, monitoring, retry logic, and error handling.
- Adopted Amazon S3 as the data lake foundation for raw and processed data.
- Built reusable transformation patterns to streamline ingestion job delivery.
- Used AWS Lambda and Amazon ECS as supporting services in the modernized ecosystem.
- Reduced annual data integration costs by approximately 80%.
- Reduced build and deploy time for new ingestion jobs from several months to a few days.
- Enabled more team members to manage workflows independently, reducing reliance on legacy platform specialists.
- Improved visibility, resilience, and near real-time insights for stakeholders.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 27, 2026Publisher: AWSEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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