Momentic, the company behind an AI-driven software testing platform, recently rearchitected its caching system to handle over 2 million queries per day across 20 billion total entries, while maintaining an average response latency of around 250 ms. This improvement was made possible by transitioning from PostgreSQL to the column-oriented database ClickHouse. By Sergio De Simone

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If you've ever built a search endpoint, you've hit this wall. Your query has filters, sort orders, a nested set of facets, maybe a geo bounding box. It doesn't fit in a URL, and cramming it into query string params is ugly and fragile. So you reach for POST /search , send the whole thing as a JSON body, and quietly accept that you've just lied about what the request does. It's not creating anything. It's a read. But POST is the only tool that lets you attach a body without fighting the platform. That gap finally got filled. In June 2026 the IETF published RFC 10008 , which defines the HTTP QUERY method: a new verb built for exactly this case. The two bad options Every read that needs structured input has been stuck choosing between GET and POST, and both are wrong in their own way. GET is the semantically correct choice. It's safe (the client isn't asking to change anything), it's idempotent (retrying it is fine), and it's cacheable. The problem is the body. RFC 9110 is explicit that content in a GET request has no defined semantics , and sending one may cause some implementations to reject the request. So your query has to live in the URI, where you run into unknown length limits across proxies and servers, encoding overhead, and the query landing in access logs and browser history. POST solves the body problem and creates a new one. It carries any payload you want, but it's neither safe nor idempotent by definition. Intermediaries won't cache it, clients won't retry it automatically after a dropped connection, and anything inspecting traffic has to assume the request might have side effects. You get the body, you lose everything that made the request honest. QUERY is the missing third option: a method that carries a body and keeps the semantics of a read. What QUERY actually is The spec, authored by Julian Reschke, James Snell , and Mike Bishop, describes it in one sentence: A QUERY requests that the request target process the enclosed content in a safe and idempo

In June 2026, a project quietly entered the Apache Incubator that I believe will matter as much to the next decade of data as Iceberg mattered to the last one. It is called Apache Ossie, it was formerly known as the Open Semantic Interchange, and its job is to standardize something the data industry has never standardized: what our data actually means. I have personal stakes to declare, as always. The proposal's champion, Jean-Baptiste Onofré, is a colleague of mine at Dremio, and Dremio is one of the three companies named as core developers of the project alongside Snowflake and dbt Labs. I have been writing about the Open Semantic Interchange since its launch, I flagged its trajectory in my recent articles on Apache Polaris and on agentic AI standards, and watching it arrive at the Apache Software Foundation feels like watching a prediction come true faster than I predicted it. So no, I am not neutral. What I can offer instead is what I always offer: the receipts, the honest caveats, and an explanation built so that anyone can follow it. That last part is the real mission of this article. Ossie lives in a corner of the data world, semantic layers, metrics definitions, ontologies, that even seasoned engineers find foggy, and most coverage of it assumes you already speak the jargon. I want to do the opposite. By the end of this piece, you should understand what problem Ossie exists to solve, what the project actually contains, why it needed a vendor-neutral home, what incubation at the ASF means, and why the timing, in the middle of the AI agent wave, is not a coincidence. No prior semantic-layer knowledge required. The Problem, Told as a Story Everyone Has Lived Forget technology for a moment and consider a Monday morning at a perfectly ordinary company. The head of marketing walks into the leadership meeting with a slide: monthly active users are up 12 percent. The head of product follows with her dashboard: monthly active users are flat. The CFO, working from the