Sales discovery
Qualification signals, pains, architecture context, and next steps — before the deal goes cold.
recommendation-engine sync — architectureCurrentTo-Be
Event Stream
Kafka / MSK
event stream
KEEPStream Processing
Flink
stream processing
NEWFeature Storage
DynamoDB
online feature store
NEWOffline Store
training parity
RECOMMENDEDContracts & Quality
Schema Registry
event contracts
RECOMMENDEDDrift Monitoring
features + model
RECOMMENDEDLegacy Pipeline
PAIN
Batch ETL
nightly · 24h stale
RETIREDModel Inference
SageMaker
live inference
KEEP Picture complete · 3 gaps filled
Coach 8/10 · 2 risks flagged
Executive brief ready · 3 decisions
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Paste a transcript fromOtterFathomGranolaZoomTeams8 file formats
The architecture[00:03:12] Maya (PM): recommendations are a full day stale, the nightly batch pipeline can't keep up with catalog changes [00:03:41] Dev (SA): right — so events already land in Kafka today, MSK on the ingestion side [00:04:02] Dev (SA): if we stream through Flink we can compute features as events arrive instead of waiting for the 2am run [00:04:30] Ana (Data): where do features live then? the model reads from the warehouse right now [00:04:47] Dev (SA): DynamoDB as an online feature store — single-digit-ms reads, SageMaker scores against it directly [00:05:15] Maya (PM): what happens to the batch job? [00:05:22] Dev (SA): it retires. one less 2am page. [00:05:40] Ana (Data): we'd need backfill for cold-start items — flagging that as a risk [00:06:03] Maya (PM): okay — decision: we prototype the streaming path this sprint, Ana owns the backfill question
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