Realtime Data Activation - Analytics, Intelligence & Decision Making
summary
Practical plan to ship realtime data: capture events with Kafka/Fluentd or Kinesis, land raw files in a cheap data lake, build purpose-built data marts with Spark/Hive on EMR or simple scripts, and serve realtime analytics with Druid answers in <1s (ingest via Tranquility with ~30s soft window, batch backfill from EMR). Train models with H2O or Spark ML and expose them as HTTP endpoints. Emphasize off‑the‑shelf systems, small teams, and the rule limit to five logos to actually deliver.
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chapters (8)
- 0:00Introduction and speaker background
- 4:47Practical data activation challenges
- 8:43Capturing data at scale
- 13:42Lambda architecture explained
- 19:07Business intelligence tools
- 24:52AI and predictive models
- 29:37Summary and key takeaways
- 31:05Disclaimers and execution advice
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