Architecture notes on distributed data platforms, cloud reliability, Spark and EMR, ML infrastructure, observability, and the production tradeoffs behind modern AI systems.
Practical lessons from Spark, Hadoop, EMR, Hive, HBase, Glue, Athena, Kinesis, and production-scale data systems.
Notes on the systems beneath AI: storage, compute, orchestration, pipelines, inference, observability, and reliability.
How complex systems fail, how to debug them, and how to design architectures that survive real production pressure.
Deep technical writing for engineers, architects, and teams building cloud, data, and AI systems in production.
A practical look at the failure modes that show up when Spark, storage, schedulers, and humans meet production scale.
How I think about choosing the right AWS data architecture based on workload shape, operations, cost, and team maturity.
LLMs look magical at the top, but underneath are queues, schedulers, storage, GPUs, caches, metrics, and reliability tradeoffs.
Built at Scale is a technical site by Raja Mannem focused on cloud, data, distributed systems, and AI infrastructure.
Production architecture, distributed systems debugging, Spark and EMR, data platform reliability, AI/ML infrastructure, observability, cost-performance tradeoffs, and engineering lessons from operating complex systems.