Streaming Data Pipelines Studio
Build incremental ingestion flows with mentor-reviewed pull requests instead of copy-paste notebooks.
Duration: 6 weeks · Format: Cohort
Vendor alignment: Data engineer certification path
Skill level: Intermediate
Practice labs: Included
Informational price: 540,000 KRW
Overview
You will fork a starter repo, implement windowed aggregations, and open pull requests that mentors treat like production reviews. Comments focus on observability hooks and schema drift handling, two topics that certification panels love to probe.
What is included
- Git-based workflow with protected branches
- Automated linting rules that mirror enterprise style guides
- Office hours on reconciling late-arriving events
- Pairing slots for debugging backpressure issues
- Documentation sprint on runbooks for on-call engineers
Outcomes
- Merge four reviewed PRs with passing CI checks
- Document one rollback path for a streaming job
- Present a five-minute demo without reading from slides
Lead mentor
Sora Kim
Data platform engineer who still on-calls once a quarter to stay grounded in incidents.
FAQ
Which languages are used?
Primarily SQL and a JVM language for streaming jobs. You can request Kotlin instead of Java during week one.
Is Kubernetes knowledge assumed?
We expect you to understand pods and services at a conceptual level. A primer is provided but not taught from zero.
Refund timing?
See Returns & Refunds on the imprint page for eligibility windows tied to cohort start dates.
Experience notes
Streaming Data Pipelines Studio was the first time my PR comments mentioned lag metrics instead of style nits.
I wanted more coverage on batch backfills. Still, the mentor pointed me to two vendor docs that clarified the gap.
The documentation sprint felt like a real sprint—messy, iterative, and useful.