Hi, I’m Ory Band

I’m a Backend engineer and hands-on Tech Lead, specializing in data-intensive processing, distributed systems, and cloud infrastructure.

I love designing and implementing intricate systems, and take to heart deploying and maintaining them once they reach production.

I’m good with making sense of things: Understanding how applications behave, perform, exposing and analyzing key metrics, as well as deep dive into difficult to crack problems.

I’m a team player, and I get the job done.

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, proving it is possible to utilize available computing capabilities of web browsers (and mobile!) as MapReduce workers for distributing donation of idle computation projects like SETI@Home and Folding@Home. Master handled 100k/tasks/min.

Things I Wrote

Collecting User Data and Usage

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.

Twitter Thoughts and Discussions

Database Architecture & DynamoDB
A deep dive into Amazon’s groundbreaking DynamoDB paper from 2007, exploring how Amazon solved massive scale challenges by building their own database. Key insights include prioritizing eventual consistency over immediate consistency, and choosing liveness (keeping the app running) over safety (preventing data divergence) - foundational decisions that shaped modern NoSQL databases.

Engineering Literature: “Designing Data-Intensive Applications”
After 3 years and 550 pages, confirmed this book truly deserves its reputation as the “backend engineering bible.” Highly recommends the audiobook format for maintaining reading pace through complex distributed systems concepts.

Database Selection Strategy
Shared practical guidance on choosing the right database for specific problems, with focus on distributed database considerations and decision frameworks for system architects.

Performance Estimation Resources
Highlighted essential napkin-math techniques for estimating system performance, including computation times, compression rates, serialization costs, and geographical latencies - crucial for back-of-envelope calculations in system design.

Leadership in Engineering Teams
Discussed applying Simon Sinek’s leadership principles in engineering contexts, emphasizing the importance of collaboration, mentoring, and understanding the “infinite game” mindset in both people management and code development.