HRISHIKESH BHARDWAJ / ENGINEERING NOTES
Architecture, experiments,
and what I learned along the way.
Course and client work. The source is private; these are my own notes on building it.
Capstone
eParts Agent Harness
Meeting transcripts in, requirements and design records out, until an agent committed small talk as requirements. Where people had to stay in the loop.
Semester project
Twitter Analytics
20,000 requests per second at $0.31/hour in Phase 3, plus a separate 70,000-RPS Redis-only experiment that traded cost for throughput.
Course projects
AWS Foundations
A Wikipedia pageview pipeline and a reusable Terraform workspace taught me to make correctness, reproducibility, and cost part of the same design.
Elastic Scaling
I built an AWS scaling controller, then moved the same decisions into an Auto Scaling Group—balancing responsiveness, instance-hours, and what the monitoring data could actually tell me.
WeCloud Chat
I took supplied Spring services through containers, Helm, GKE, and AKS, making the differences between clouds explicit while keeping one understandable application definition.
Cloud Storage
A week with Yelp data, SQL, MongoDB and Redis—and the small ownership check that made a distributed lock worth thinking about.
Social Network Storage
How I assembled a social feed from MySQL, Neo4j and MongoDB, then added a cache policy I could actually observe.
Uber Ride Matching
More than 10,000 events per second, and 30% lower rider-match latency, through a Kafka and Samza backend: partitioning by city block, tracking driver availability, and joining live events with rider profiles.
NYC Fare Prediction
Distance, time, airport trips, and the feature-engineering choices behind a cloud ML project that grew toward a voice-driven NYC assistant.
CloudTube Media Pipeline
An event-driven media pipeline across Azure Functions, Blob Storage, AI Vision, and Search—and the runtime details that made the handlers more careful.