Real-Time Analytics Platform
From batch reports to real-time insights
An e-commerce platform client
An e-commerce operator was running the business off a 24-hour-old data warehouse. By the time they noticed a stockout or a checkout regression, the day was already lost. I designed a streaming analytics platform that ingests order, inventory, and clickstream events directly from production, aggregates them in seconds, and surfaces them in dashboards operators actually open. The hard part wasn't volume - it was correctness under late-arriving data and a UI that non-engineers trust.
This is a representative architecture study based on real project patterns. Specific metrics and client details have been generalized to protect confidentiality.
Results
What changed, in numbers
The metrics the engagement is measured by.
<1 second
Data Latency
from 24-hour batch delay
10M+
Events Processed
events per minute at peak
+12%
Revenue Impact
from faster operator decisions
95%
Adoption
daily active operators
Challenge
What was broken
Decisions were 24 hours behind reality. Stockouts were detected the next morning, fraud spikes during peak shopping windows went unnoticed, and merchandising experiments couldn't be evaluated until the next day. The existing batch warehouse was perfectly fine for finance and BI but useless for operations. A naive 'just stream everything' approach would have created an operational nightmare.
Solution
The shape of the fix
A streaming analytics platform that processes 10M+ events per minute end-to-end, with sub-second dashboard latency, late-event reconciliation, and a self-serve query layer that put data into the hands of the people making merchandising decisions.
Approach
How I tackled it
The concrete moves that took the project from broken to shipped.
Built an event-streaming backbone with strict schema contracts so producers can't break consumers
Used a columnar real-time database for sub-second aggregation across millions of events per minute
Designed for late-arriving and replayed events from day one - dashboards reconcile, they don't lie
Surfaced metrics over WebSockets to a Next.js dashboard so operators see counters tick live
Added a self-serve query builder so the merchandising team could answer their own questions
Hooked alerts into the team's existing on-call rotation so anomalies create tickets, not Slack noise
Outcomes
What shipped, and what it changed
Measured results from the engagement, told as a story rather than a scoreboard.
Cut data latency from 24 hours to under 1 second on operational metrics
Sustained 10M+ events per minute through the streaming pipeline at peak
Drove a measured 12% lift in revenue from faster reaction to stockouts and demand spikes
Reached 95% daily active usage among operations and merchandising teams
Reduced 'why is this number wrong' Slack threads to near zero by making reconciliation visible in the UI
Stack
Technologies used
Linked entries open the technology page with related studies, playbooks, and notes.
Services
How I helped
The specific services involved in this engagement. Each links to a deeper breakdown.
Lessons
What I would tell the next team
The takeaways I carry into every similar engagement.
Real-time is a UX problem as much as a pipeline problem. If operators don't trust the number, they'll go back to the spreadsheet
Schema contracts at the producer are non-negotiable. Without them, a streaming platform becomes a streaming outage
Build for replay from day one. You will need it
Related
Other studies you might recognize
Engagements with overlapping problem shapes, industries, or stacks.
Have a similar challenge?
If any of this looks like the project on your desk, the conversation is the cheapest part. You can also browse other e commerce work or the full service list.