Resources
Databricks made a significant announcement at Data + AI Summit this week. LTAP, or Lake Transactional/Analytical Processing, is their answer to a problem the industry has been trying to solve for decades: how do you run transactional and analytical workloads...
TL;DR - RegattaDB runs four applications on the hardware you'd normally use for one, without pipelines, without extra...
Things are moving fast. The market has shifted from "should we build agents" to "we are building and will...
The legacy data stack can’t keep up Databases are architected for one thing. You pick Postgres for...
SUMMARY: The Shift to Agentic AI is real: Moving beyond basic LLMs, the Agentic Era focuses on AI systems that can...
This post documents a TPC-C benchmark for RegattaDB at 1.5 million warehouses across 50 GCP nodes, sustaining over 750,000 transactions...
Distributed joins are among the most demanding operations in large‑scale data systems. Trying to do this at the same time...
Agents need to both think and act The core objective of Agentic AI is to deliver systems capable of executing functions that...
Introduction In this post, we’ll cover how RegattaDB’s concurrency-control protocols (CCPs) enable full serializable isolation in a distributed database...
In this tutorial, we'll show you just how simple it is to deploy a Spring Boot application that interacts with Regatta...
In this blog I will provide a high level overview of Regatta’s design and architectural principles. While I keep this introduction...
A few years ago, Erez Webman and I wrote a white paper describing the dirty little secrets of scale-out sharding....
While feature-rich, scale-out products may sound complex to manage, when designed right and with architectural simplicity at the...
Regatta clusters scale from a single node to tens of thousands of nodes with hundreds of petabytes of data. Unlike traditional scale-out...