Writing

9 published, more on the way. Grouped by year.

2026

Jul 7 Why I started writing A home for my notes on AI, data, and building teams that ship. Apr 15 Notes from GTC and Cloud Next: we have stopped talking about just models Reflections from NVIDIA GTC and Google Cloud Next 2026 on the shift from AI models to AI systems that hold up in production.

2024

Jul 22 Micro frontends: when the best thing to share is the app itself Integration between teams keeps moving up the stack, from nightly files to streams to APIs. Micro frontends are the next step, and here is why they cut duplicate work and inconsistency.

2023

May 18 Scaling an ML algorithm with Spark as distributed compute An ML algorithm written in Python had to run over tens of millions of items. Here is why I ran it as a distributed Spark job instead of an API or an event stream.

2022

Sep 14 Challenges of real-time streaming in Druid Coming soon The hard parts of streaming ingestion at scale, from late data to exactly-once, and how I handled them.

Feb 2 Performance optimization for Apache Druid Coming soon Tuning segments, rollups, and queries to keep a high-volume analytics product fast at scale.

2021

Nov 20 Performance testing, part 2: publishing k6 metrics with InfluxDB and Grafana Once k6 was running, results vanished after each run. Here is how InfluxDB and Grafana turned load testing into something we could track and share. Sep 20 Why k6 for performance testing over JMeter and LoadRunner Moving load testing into code with k6 so any engineer could run it, and why it beat the traditional tools for our workload. Jun 15 Why Druid: building a real-time analytics product for tens of thousands of users How I evaluated MongoDB, Cassandra, HBase, and Apache Druid for a sub-second, high-concurrency analytics product, and why Druid won. Mar 10 Why I chose Micronaut over Spring Boot (and Kotlin along the way) Picking a JVM framework for a high-throughput, low-latency data product, and why Micronaut and Kotlin were the right call.

2020

Feb 15 A Phoenix on HBase problem, and how we worked through it Coming soon A hard Phoenix and HBase issue that surfaced in late 2019 and ran into early 2020, and what it taught me.

2019

Apr 14 Reporting Kafka and Spark streaming metrics with a custom Spark listener There is no ready-made dashboard for Kafka to Spark streaming health, so I built a listener to publish the metrics to InfluxDB and Grafana.