i spent a year hand-building visual explainers in cursor. then i tried to automate it with ai. the first month was all agent infrastructure and zero visuals.

a real incident walkthrough for when your system is healthy and completely wrong

a practical explanation of snapshots, delta lake, apache iceberg, and why data teams suddenly care about table formats.

the art of removing costs you didn't know you were paying on every row

achieve harmony in complex clusters using finite-state machines

unleashing the full potential of your rocksdb with the right configuration

before diving into this article, i should state that — as a developer — any situation requiring placing a language a project into a language b package should occur very rarely. most of the time it’s preferable to consider re-designing the interaction between various language components in these situations. but what if this situation is unavoidable? open source projects such as apache flink and apache spark serve as examples. these projects have been written completely in java but also have python modules available for those who don’t want to use the java api.

groovy functions

one of the primary advantages of using pinot is its pluggable architecture. the plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.

flood

the lab

leader and followers

a glimpse at capabilities which makes airflow better than its predecessors

explore the various strategies to deploy ml models in apache flink/spark or other realtime data streaming applications.