7 Successful Habits For Data-intensive Applications In Production
summary
Claims that MapR customers get >90% of large-scale projects into production vs Gartner's ~17% and explains why, with concrete practices: treat raw data as production-ready with snapshots and versioning, use a data fabric spanning edge to cloud, combine Kubernetes orchestration with persistent storage so containers keep state, adopt stream-first architectures (A/B testing by subscribing to the same stream), collapse many HBase clusters into one and use MapR streams (Kafka API) for replayable event logs.
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chapters (9)
- 0:00Introduction and speaker intro
- 2:27Treat data as production early
- 7:20Design for business goals
- 12:23Balance commonality and isolation
- 14:29Orchestrate apps and data
- 18:47Simplicity is golden
- 23:00Management and multi-tenancy
- 25:50Stream-first architecture
- 28:19AI and machine learning culture
