Sam Arustamov

Megaladata is a high-performance visual data platform built on nearly three decades of engineering focused on one question: how much can we get out of the hardware we already have? Modern machines are incredibly powerful, yet data platforms often fail to use that power efficiently. The problem is not necessarily slow algorithms. It is everything around them: how data is represented, copied and moved, how memory is allocated, how CPU cores are utilized, and how intermediate results travel through a workflow. In this session, we’ll go under the hood of Megaladata and explore some of the engineering decisions behind its performance: specialized data representations, in-memory processing, memory management, parallel execution, and minimizing unnecessary data movement. The session challenges a common assumption: when data grows, the answer is simply more hardware or a more complex distributed stack. Before scaling your infrastructure, there is another question worth asking: how efficiently is your data platform using the hardware you already have?