How Stockland accelerated data-driven insights on AWS
summary
Stockland re-architects its on‑prem data warehouse to a cloud native AWS platform and migrates the core estate under six months, landing Amazon S3 as the data lake and using S3, Redshift, SageMaker plus CI/CD and serverless orchestration. ETL runtimes and infra costs fall, model time‑to‑production drops from ~nine months to models deployed in weeks, and the team delivers self‑service BI, ML pipelines, and bespoke data‑driven apps. Emphasis on use‑case driven ingestion, automation, decoupled compute/storage, and product management keeps solutions maintained and adopted.
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chapters (11)
- 0:00Introduction and speakers
- 1:18Stockland’s data ambitions
- 3:53On-premise data challenges
- 5:41Design principles for modern platform
- 8:52Implementation phases explained
- 12:28Target state architecture
- 14:15Core services powering platform
- 16:48Project outcomes and KPIs
- 19:24Key learnings from journey
- 24:42Future plans and next steps
- 27:13Closing and resources
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