Streaming

Event time vs processing time, windowing, watermarks, delivery guarantees and state. This path sequences the category's 8 resources by roadmap stage and composite score — work top-down inside each step.

Streaming

1. Lambda vs Kappa (batch + stream) 34.75 — Lambda vs Kappa (batch + stream)
2. Hold the streaming mental model: events, partitions, delivery semantics 34.15 — Hold the streaming mental model: events, partitions, delivery semantics
3. Make the sink idempotent so replays never duplicate rows 34.15 — Make the sink idempotent so replays never duplicate rows
4. Window a stream correctly — watermarks, dedupe, and late data 34.15 — Window a stream correctly — watermarks, dedupe, and late data
5. Streaming Dedup and Upsert 31.75 — Streaming Dedup and Upsert
6. Kafka → Spark Streaming → Delta 31.65 — Kafka → Spark Streaming → Delta

2 more on the category page.

• Explain why ordering and partitions behave the way they do
• Treat at-least-once delivery and replays as the normal case
• Choose between lambda, kappa, and plain batch for a latency and reprocessing need
• Make the sink idempotent rather than trying to make the source exactly-once
• Deduplicate by a business key + event time within the watermark
• Rank change events deterministically by (lsn, ts_ms), never by arrival time
• Choose event-time vs processing-time windows
• Set a watermark and reason about late data

Security-flagged: Streaming Dedup and Upsert, Kafka → Spark Streaming → Delta, Kafka Streaming Job Template — review the security posture first. Verification on these records: 8 community. Nothing here is independently audited — the trust scan and your own review are the controls.

Phase 2 of the roadmap. Stage context: Streaming · side-by-side comparison · all learning topics