Hi, I'm Ory Band
I'm a backend engineer, specializing in distributed, data-intensive systems, as well as cloud infrastructure.
I know how to design, implement, deploy, monitor, and maintain distributed applications at scale.
I like to code challenging projects with lots of moving parts, educate, and write for fun in my blog.
I'm also into Golang, Clojure, and decentralized technologies (blockchains).
Things I built
Analytics ETL Pipeline
Implemented an event streaming and ETL pipeline. System was highly available, resilient, and with low response time. Written in Golang and deployed on Google Cloud. Peak load reached 1 billion req/day (= 700k req/m). Analytics were stored in various data stores including Google BigQuery, Elasticsearch, and Prometheus.
Production Monitoring Infrastructure
Developed a distributed production monitoring infrastructure, spanning multiple cloud providers.
Federated Blockchains
I gave a talk about Federated consensus networks, focusing on Stellar and Ripple, and discussed how they differ from other popular decentralized consensus solutions such as Proof-of-Work and Proof-of-Stake.
Google BigQuery open source library
Released an open-source Go library for inserting data to Google BigQuery at scale. Project received ~130 GitHub stars, and was recognized by core Go and BigQuery team members at Google.
Decentralized Networks
Deployed and maintained multiple decentralized networks for production, research, and testing on the cloud.
Web MapReduce
I implemented a proof of concept for a web-based MapReduce in Go, aimed at utilizing the free computing capabilities of web browsers as MapReduce workers, specifically mobile phones.
Things I Wrote
Knowing what our users are doing with our app is important — What they like, what they don’t, quality of our video calls, etc. Gathering and storing this information however, is quite a task — especially when we have more than one million events reported every minute. At Rounds, we are using two data stores for live monitoring, search, and BI. One is indeed for immediate, live data, and the other for long-term data warehousing and long-term research.