Do I need Flink, or will Spark Structured Streaming do?

Both can work. If you need low-latency stateful processing with fine-grained backpressure and native exactly-once sinks, Flink 1.18 is hard to beat. Spark Structured Streaming is great when you already run Spark and your latency SLO is ~seconds, not tens of milliseconds.

Is Confluent Kafka required?

No. We’ve deployed on OSS Kafka, Redpanda, and Pulsar. Confluent adds managed ops and a mature Schema Registry. Pick the platform your team can operate reliably. The patterns—contracts, EOS, DLQ, replay—matter more than the vendor.

How do you handle GDPR deletes in a streaming world?

Emit delete commands to a dedicated `privacy.deletes` topic keyed by user ID. Apply them in-stream to your online stores and periodically run compaction or merge operations in your lakehouse/warehouse to hard-delete historical records.

What’s the fastest path to value if we’re all batch today?

Start with one revenue-impacting use case (e.g., recommendations freshness). Stand up the log, a single Flink/Spark job, and one sink. Prove p95 99.9% for that path before you boil the ocean.

Data-engineering · Oct 2, 2025 · 10 minute read

From 8‑Minute Lag to 30‑Second Insights: A Streaming Data Backbone That Doesn’t Flinch

You don’t need more buzzwords. You need a streaming architecture that survives traffic spikes, keeps your data clean, and ships value on time.

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From 8‑Minute Lag to 30‑Second Insights: A Streaming Data Backbone That Doesn’t Flinch

Key takeaways

Implementation checklist