Discover how SafetyCulture, the global workplace operations platform used by 70,000+ organizations, achieved a 90% reduction in daily data pipeline execution time while processing the same data volumes on Amazon Redshift. Join this session with SafetyCulture data engineering team to learn how their team transformed a complex, slow-running data warehouse into a high-performance, AI-ready analytics platform using modern lakehouse architecture principles.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Lakehouse modernisation on Redshift achieving 90% reduction in pipeline execution time on the same data volumes. Modern lakehouse architecture principles.
Why it matters
Pipelines that take all night kill iteration speed for analysts and data scientists. AI workloads need fresh data, not yesterday's.
The hard parts
Old SQL doesn't translate cleanly to lakehouse patterns. Materialisation strategies, incremental loading, partition design — all need rethinking.
Playbook moves
(1) Identify "tall pole" pipelines (the slowest 5%) first — they're usually 50%+ of total runtime. (2) Rewrite those for materialised views + incremental loads, not full table scans. (3) Measure end-to-end, not per-job.
The surprise
Most pipeline 90% wins come from *removing redundant computation* (the same aggregation computed three times across three pipelines), not from faster compute. Profile before re-platforming. You'll find that half the pipelines are doing the same work.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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