You don’t get a #YugabyteDb by distributing the storage layer of a SQL database. You don’t get a #Neo4j by building a graph model on top of your RDBMS database. You don’t get a #ScyllaDb by rewriting Cassandra into C++. You don’t get a #DuckDb by optimising SQLite for analytical queries. To truly elevate a system to a 'gold standard', it's essential to apply specialized techniques and thoughtful implementations at every layer, not just one. Focusing on a single layer may seem like a solution, but it merely shifts the problem elsewhere, often introducing new challenges in the process. It's like driving an electric vehicle charged by coal-powered electricity. Iykwim ps: I am not comparing any system with coal. Coal is great unless burned. 😂 To know more about how the above systems are built checkout below links: DuckDB Internals with Mark Raasveldt @duckdb3282 youtu.be/f9QlkXW4H9A ScyllaDB internals with Felipe Mendes youtu.be/AqY13RjWwJg Graph Database Internal Neo4j with Michael Hunger youtu.be/iihJXKAQZkA Yugabyte Internals with Franck Pachot youtu.be/cXIPIA7e220

This is out now: "What Every Developer Should Know About GPU Computing" open.techwriters.info/codeco… It took longer than anticipated but good things usually do take time. Big thanks to @msharmavikram for reviews and suggestions, his feedback added another level of depth. GPUs are a very different beast, and as an outsider you will encounter a ton of new terms and concepts you have never heard about. This article won't make you an expert because for that you need to read a whole book or take a course on this topic. However, if you wanted to learn how do GPUs work and what distinguishes them from CPUs, then this article is for you. Here's what you will learn: - A high level comparison between CPU and GPU architectures - A deeper look at the GPU compute and memory architecture - How a kernel executes on the GPU - Dynamic resource partitioning and Occupancy I hope you enjoy it. I will try to share a summary here later.