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Data Engineering for EdTech

Real-time learning analytics at global scale — we took a Learning Experience Platform serving millions of learners from nightly batch to sub-3-minute streaming.

Problems We Solve

Industry Challenges

Stale learner data

Nightly batch means educators see yesterday's engagement. We rebuilt a global LXP's data spine on Confluent Kafka + CDC — end-to-end latency under 3 minutes for millions of learners.

Engagement signals buried in event volume

Clickstreams, video progress, assessments — billions of events. BigQuery lakehouse design with streaming ingestion turns volume into per-learner engagement scores.

Search and discovery analytics

Learners expect instant content discovery. ElasticSearch drill-down layers synced from the lakehouse power both product search and content-performance analytics.

Seasonal scale

Enrolment peaks crush fixed infrastructure. Serverless-first design (BigQuery, Cloud Functions, Pub/Sub) scales with semesters and contracts back after them.

Proven In Production

Measured Results

<3min
End-to-end latency
from nightly batch
Millions
Learners served
global LXP
10x
Scalability
serverless architecture
Evidence

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Questions, Answered

EdTech FAQ

How fresh can learning analytics realistically be?
Sub-3-minutes end-to-end is production-proven: CDC from operational databases through Confluent Kafka into BigQuery, with Cloud Functions syncing DOMO dashboards and ElasticSearch. Educators act on this morning's engagement, not yesterday's.
Can you handle billions of learner events affordably?
Yes — streaming ingestion into partitioned BigQuery with lifecycle tiering keeps cost linear-ish while volume grows 10x. FinOps attribution shows cost per product team, not one scary bill.
Do you work with both K-12 and corporate learning platforms?
Yes — the architecture patterns (event streaming, engagement scoring, content analytics) transfer across LXP, LMS, MOOC, and corporate L&D platforms; compliance posture adapts (COPPA/FERPA contexts vs corporate privacy).
Can product and BI teams share one platform?
That's the design goal: a lakehouse serving product features (search, recommendations) and business analytics (retention, content ROI) from the same governed data — no more divergent numbers between product and finance.
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