Data Pipelines

Streaming Data Mesh Lab

Backpressure-aware ingestion, schema contracts, and incremental feature stores for teams shipping near-real-time ML features.

Duration
5 weeks · online
Format
Live labs
Level
Advanced
Tuition (informational)
₩1,250,000
Streaming Data Mesh Lab

Program narrative

We build a reference mesh with bounded queues, dead-letter handling, and schema evolution playbooks. Labs stress failure injection so you can narrate what happens when a broker partition pauses mid-day.

What is included

  • · Contract testing for Avro/JSON schemas with CI snippets
  • · Lag dashboards that separate consumer drift from upstream stalls
  • · Feature materialization windows with explicit freshness SLOs
  • · Replay harness for debugging ordering assumptions
  • · Cost guardrails worksheet for bursty traffic
  • · Pair reviews on your own topic definitions
  • · Lightweight governance checklist for cross-team ownership

Outcomes you can demo

  • · Stand up a replayable ingestion branch with documented RPO/RTO assumptions
  • · Draft a schema migration note your data platform team can approve
  • · Facilitate a blameless review using the provided incident template

Mentor of record

Sora Kwon

Sora Kwon

Built event pipelines for mobility telemetry; teaches with an emphasis on operable documentation.

Participant questions

Will we touch managed cloud services?

Yes, with vendor-agnostic patterns called out. You may mirror exercises in your employer cloud with instructor guidance.

Is there certification?

You receive a completion certificate describing projects submitted. It is not an industry credential exam.

Limitations?

We do not tune hardware-level NIC offload or bespoke kernel modules. Those belong to vendor-specific advanced courses.

Recent participant notes

“The replay harness in Streaming Data Mesh Lab mirrored a production bug we had filed as “ghost latency.” Finally reproducible.”
— Marta · Logistics analytics · Google
“Clearer than internal docs. Still wish we had 30 more minutes on Iceberg compaction trade-offs.”
— Devon · Data engineer · 4/5
“Client in fintech — cohort policies on PII were respected; templates were easy to scrub for our compliance pack.”
— Anonymous