{"slug": "meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf", "title": "Meet Apache Ossie: The Open Semantic Interchange Finds Its Home at the ASF", "summary": "Apache Ossie, formerly the Open Semantic Interchange, has entered the Apache Incubator to standardize the meaning of data across organizations. The project, backed by Dremio, Snowflake, and dbt Labs, aims to solve semantic drift, a problem exacerbated by AI agents querying data without consistent definitions. Its vendor-neutral home at the ASF is timely as AI agents become more prevalent in data analysis.", "body_md": "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.\n\nI 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.\n\nThat 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.\n\nForget technology for a moment and consider a Monday morning at a perfectly ordinary company.\n\nThe 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 finance team's warehouse queries, has them down 3 percent. Three smart teams, three careful analyses, three different numbers for what sounds like one simple thing. The next forty minutes of the meeting are spent not making decisions but arguing about whose number is real.\n\nHere is the uncomfortable truth: nobody in that room is wrong. Marketing counts anyone who opened the app. Product counts anyone who performed a meaningful action, and excludes internal test accounts. Finance counts paying seats, and their pipeline lags a week. Each definition is defensible. The problem is that the company never wrote down which one is the definition, in a form all their tools could share, so every tool and team quietly invented its own. The industry has a name for this disease: semantic drift. The same business concept, defined inconsistently across an organization's systems, drifting further apart with every new tool, every new hire, every new dashboard.\n\nEvery data professional reading this has lived some version of that meeting. What is new in 2026 is who else attends it. Companies are now pointing AI agents at their data and asking questions in plain English: what is our churn rate, how did the campaign perform, which customers are at risk. An agent asked to calculate churn looks at the warehouse and finds three tables that might be relevant, several plausible formulas, and no way to know which one the business considers true. A human analyst in that situation walks down the hall and asks someone. The agent just picks one, confidently, and the result is a plausible-looking number computed with logic no one endorsed. Multiply that by every question every agent answers every day, and semantic drift graduates from a chronic annoyance to an acute liability.\n\nThe old costs of drift, the meeting arguments, the weeks engineers spend manually reconciling definitions between systems, the migration projects that stall because business logic is trapped inside one vendor's tool, were bad enough that the industry tolerated them for decades anyway. The AI cost is the one that finally forced the issue. That is the context in which the Open Semantic Interchange was born, and it is the context in which it just became Apache Ossie.\n\nBefore I can explain Ossie, I need to demystify the thing it standardizes, because \"semantic model\" is one of those phrases that sounds far more exotic than it is.\n\nA semantic model is simply the company's data dictionary, written precisely enough that software can use it. It captures a few kinds of things. Metrics: the named numbers the business runs on, revenue, churn rate, monthly active users, each with its exact formula, the filters that apply, and the grain it is measured at. Dimensions: the ways you slice those numbers, by region, by month, by product line, by customer segment. Entities and relationships: the nouns of the business, customers, orders, subscriptions, and how they connect, including the unglamorous but vital facts like \"the sales system's Customer_Code and the billing system's Account_UID refer to the same customer.\"\n\nConcretely, a semantic model entry for our troubled metric might say, in a structured, machine-readable form: monthly active users is defined as the count of distinct users who performed at least one qualifying action in the trailing thirty days, excluding internal accounts, where a qualifying action is any event in this named list, measured against the events table in the analytics schema. Written down like that, the definition stops being folklore. A BI tool can read it and build the dashboard from it. A data engineer can test against it. An AI agent can quote it and compute with it. And when the business decides to change the definition, it changes in one place and every consumer follows.\n\nNone of this is a new idea. BI vendors have shipped semantic layers for decades, and tools like dbt made metric definitions a first-class artifact for the modern stack. The problem was never that semantic models did not exist. The problem is that every tool spoke its own dialect. The definitions in your BI tool's semantic layer, your dbt project, your catalog's glossary, and your CRM's configuration are written in incompatible formats, cannot read each other, and therefore drift apart, which lands us right back in the Monday meeting. Worse, because your business logic is trapped in each tool's proprietary format, leaving any tool means rewriting your company's brain from scratch, which is a form of lock-in more binding than any data format ever was.\n\nIf this shape of problem sounds familiar to readers of my other work, it should. Data files had this problem before Parquet. Tables had it before Iceberg. Catalogs had it before the Iceberg REST protocol and Polaris. In every case the cure was the same: stop standardizing the tools, standardize the interchange. Ossie is that cure applied to meaning itself.\n\nNow the project itself, concretely, because Ossie is refreshingly tangible for something with \"semantic\" in its description. Open the repository at github.com/apache/ossie and here is what you find.\n\nAt the center sits the core specification: a vendor-neutral format, expressed in YAML and JSON, for writing down semantic models, the metrics, dimensions, entities, and relationships we just discussed, in a way any tool can read and write without loss of meaning. The spec ships as human-readable documentation alongside machine-readable schemas, so a tool builder can validate that a model conforms. This is the heart of the project: not software you run, but an agreement about how meaning is written down, the same way Parquet is fundamentally an agreement about how bytes are laid out.\n\nAround the spec sit the pieces that make an agreement practical. A converters directory holds reference translators between Ossie and the existing dialects, with converters for dbt, GoodData, Salesforce, and Apache Polaris already in the tree, because a standard nobody can migrate to is a whitepaper, and the entire adoption path runs through translating what companies already have. A validation directory holds tooling to check models against the schema. An ontology directory holds the standardized vocabulary work, the shared upper-level concepts that let one company's model be intelligible to another's tools. An examples directory includes a complete semantic model for the TPC-DS benchmark schema, which I would point to as the fastest way for a practitioner to get the feel of the format: a familiar retail-flavored schema, fully described in Ossie terms. And a Python package provides the core types for programmatic work.\n\nThe design philosophy threaded through all of it deserves a sentence of appreciation. Ossie's architecture is decentralized: systems read semantic metadata from the source that owns it rather than depending on point-to-point field mappings between every pair of tools. The proposal states the contrast plainly, and it echoes everything I have written about the N times M problem in the agent standards world. Manual mapping between systems grows quadratically and breaks whenever a schema changes. Self-describing data, where the meaning travels attached to the source and every consumer reads the same description, converts that quadratic mess into a linear one. The dream, stated simply: define once, understood everywhere.\n\nOne more plain-language clarification, because it trips people up. Ossie does not store your data, query your data, or replace your warehouse, lakehouse, BI tool, or semantic layer product. It is the interchange format those systems use to agree with each other, the way a PDF does not replace your word processor. Your tools keep their engines and their interfaces. What changes is that the definitions inside them stop being trapped.\n\nSince the specification is the heart of the project, let me walk through what an Ossie model conceptually contains, in plain terms, so the format stops being abstract. I will describe rather than reproduce, because the spec is young and evolving, but the shape is stable enough to internalize.\n\nAn Ossie model is a structured document, YAML or JSON, that a human can read and a machine can validate. Think of it as having four kinds of entries, layered from concrete to abstract.\n\nAt the bottom are the pointers to physical reality: which datasets the model describes, the tables or views in your warehouse or lakehouse, and how the logical names in the model map to physical columns. This grounding layer is what lets a definition be executable rather than merely aspirational, and it is why the catalog integration matters so much: the catalog already knows where the tables live and who may touch them, so a model registered there inherits that grounding.\n\nAbove that sit the entities and relationships: the declaration that a customer is a thing, identified by this key, appearing in these datasets under these different column names, related to orders one-to-many, related to subscriptions through this join. This is the layer that kills the \"third system\" problem the proposal describes, where the knowledge that Customer_Code and Account_UID mean the same person lived only in an engineer's head or a stale wiki page. In an Ossie model, that equivalence is a declared, versioned, machine-readable fact.\n\nAbove that sit the dimensions and measures: the attributes you slice by, time at various grains, geography, segment, and the raw quantifiable facts, order amounts, event counts, that metrics are built from. And at the top sit the metrics themselves: named, documented calculations with their formulas, filters, exclusions, and grains, the monthly active users and churn rates that the Monday meeting argues about, now written with the precision of code and the readability of documentation. Each metric can carry its human description alongside its machine logic, which matters enormously for the AI use case: an agent gets both the formula to compute with and the prose to explain itself with.\n\nTwo properties of the format deserve emphasis because they carry the philosophy. First, models are plain text files, which means they live in version control, changes arrive as reviewable diffs, history is preserved, and the workflows every engineering team already trusts, pull requests, approvals, CI validation against the schema, apply to business definitions with no new machinery. The industry spent a decade learning that analytics logic deserves software engineering discipline. Ossie extends that lesson to meaning itself. Second, models are composable and referenceable rather than monolithic: the ambition, being worked through in the composability working group, is that a finance domain model and a product domain model can each own their pieces and reference each other, so a large organization's semantics can be federated the way its data already is, rather than requiring one impossible galactic model maintained by one overwhelmed team.\n\nIf you want to see all of this concretely, the examples directory in the repository contains a complete model for the TPC-DS schema, the industry's standard retail benchmark. Reading it is the fastest education available: familiar tables, store sales, customers, items, described entity by entity and metric by metric in the actual format. An afternoon with that file will teach you more than any article, this one included.\n\nThe institutional story is short and fast, and the speed is part of the story.\n\nIn September 2025, Snowflake launched the Open Semantic Interchange initiative together with Salesforce, dbt Labs, and a founding coalition of BI and data tooling companies, naming semantic fragmentation as the shared enemy and a vendor-neutral standard as the goal. I covered the launch with hopeful skepticism at the time, because vendor-led standards initiatives have a mixed history: some become Iceberg, and some become press releases.\n\nThis one shipped. The repository opened in November 2025. In January 2026, the v0.1 specification went live under the Apache 2.0 license, giving the industry its first neutral, YAML-based standard for metric and dimension definitions. The partner roster compounded from seventeen launch organizations to more than fifty, pulling in Databricks, ThoughtSpot, Collibra, AtScale, Atlan, and a long tail of the analytics ecosystem, and, notably, pulling in competitors of the founders, which is always the tell that a standard is real. Working groups formed with dedicated leads across five fronts: the metric language itself, composability, catalog integration, ontology, and a synchronization API. By the time of the incubation proposal, the project counted more than a hundred commits, dozens of merged pull requests from contributors across at least a dozen companies, and over seventy active design discussions in the open.\n\nThen, in June 2026, Jean-Baptiste Onofré brought the proposal to the Apache Incubator: Ossie, a data semantic specification and framework. The discussion thread ran on the public incubator list, the formal vote followed, and it passed with binding support from across the Incubator PMC. The resources spun up within weeks, the apache/ossie repository, the dev mailing list, the podling page, and the announcement JB shared publicly marked the moment: the Open Semantic Interchange had become Apache Ossie, incubating.\n\nNine months from launch to the Incubator is fast by any standard, and it reflects a deliberate strategy visible in the proposal itself: the initiative adopted Apache-style governance from the beginning, the Apache 2.0 license, a public specification process with discussion windows and votes, merit-based contribution, precisely so that the eventual donation would be a formality rather than a renovation. The people involved knew the playbook because they had run it before, which brings us to who they are.\n\nTwo questions everyone asks first, answered honestly.\n\nThe name is the easy one. \"Open Semantic Interchange\" and its acronym OSI were always headed for trouble: the acronym collides with the Open Source Initiative, one of the most established names in all of open source, not to mention decades of networking engineers for whom OSI means a seven-layer model. The proposal addresses this directly, and the community chose Ossie, keeping a phonetic echo of the original while clearing the trademark and confusion risks. Expect the phrase \"Apache Ossie, formerly known as the Open Semantic Interchange\" to do heavy lifting in the ecosystem's vocabulary for a year or so, this article included.\n\nThe deeper question is why a working initiative with fifty partners needed the Apache Software Foundation at all, and the answer is the same argument I have now made across this entire series of articles. A standard's entire value is neutrality. Companies will only pour their business logic into an interchange format if they are certain the format cannot be tilted, stalled, or captured by any single vendor, including and especially the vendor that started it. Snowflake launching OSI was necessary and commendable, and it was also, inevitably, a reason for some rivals to hesitate. Donating the project to the ASF converts \"trust Snowflake and friends\" into \"trust a twenty-five-year-old foundation whose entire constitution is preventing capture.\" The proposal says this in its own words: the interest is governance alignment, not brand decoration, because a vendor-neutral interoperability standard requires consensus-based, multi-stakeholder governance, which is what the ASF provides.\n\nFor readers newer to the Apache world, here is what incubation practically means. Ossie is now a podling: a project admitted to the Apache Incubator, operating under ASF rules, with experienced mentors assigned, in Ossie's case JB Onofré, Russell Spitzer, Holden Karau, and Zili Chen, names that between them span the Iceberg, Polaris, Spark, and broader Apache data ecosystems. Over the coming incubation period, the project must demonstrate the things the ASF certifies: that its intellectual property is clean, that its releases follow foundation policy, and above all that its community is diverse and self-governing enough to outlive any founder's interest. Graduation to a Top-Level Project, if and when it comes, is the ASF's audit stamp saying exactly that. Readers of my Polaris article have seen this movie: Polaris entered the Incubator in 2024 amid similar \"is it just the vendors' project\" skepticism and graduated eighteen months later with a contributor base that answered the question. Ossie's proposal explicitly names the Polaris playbook, community sprints, curated first issues, onboarding workshops, as its model for the same journey.\n\nRead an incubation proposal closely and the roster tells you more than the prose. Ossie's is unusually revealing.\n\nThe core development today comes from three companies: Snowflake, Dremio, and dbt Labs, with Salesforce alongside in the initial governance. Look at that list through the lens of my previous articles and something remarkable is hiding in plain sight: Snowflake and Dremio are the two companies that co-created Apache Polaris, and here they are again, joined by the company whose semantic layer work in dbt largely defined the modern metrics conversation. The initial project management committee spans all four companies, the mentors are veterans of the exact Apache data projects Ossie must integrate with, and the champion, JB, is a long-time ASF member who helped shepherd Polaris through its own incubation. The initial committer list reaches beyond the founders too, including independent contributors and names like Jochen Christ, known for the data contract movement, which signals the project understands its neighbors.\n\nThe contributor flow reaches wider still: the proposal documents merged work from Snowflake, Salesforce, Databricks, dbt Labs, AtScale, Atlan, RelationalAI, ThoughtSpot, GoodData, Honeydew, and Hex. Pause on that list. It contains direct competitors at nearly every layer, warehouses that compete with lakehouses, BI tools that compete with each other, semantic layer products whose entire commercial moat was, until now, the proprietary format Ossie replaces. When companies co-invest in dissolving their own lock-in, it is because they have concluded the market demands it, and their customers, who have spent years asking why business definitions cannot travel between tools, supplied that demand.\n\nI will state my house's position plainly, since I opened by declaring it. Dremio's involvement in Ossie is the same bet it made co-creating Arrow, championing Iceberg, and co-creating Polaris: that open standards at every layer of the stack grow the whole market and let vendors compete on their engines and experiences rather than on their customers' inability to leave. Semantics was the last major layer without such a standard. It is entirely consistent that the same crowd showed up to build one.\n\nFor readers following my series, this is where the threads tie together, because Ossie is not arriving into a vacuum. It is arriving into a stack that has been preparing a socket for it.\n\nStart with the catalog. In my Polaris article, I reported that the Polaris community had been running a high-traffic design discussion on semantic layer support and had formally voted to accept an OSI-aligned semantic model API specification. Now look at Ossie's side of the handshake: a Polaris converter already merged in the repository, catalog integration named as a dedicated working group, and deepening Polaris integration named explicitly in the proposal's future plans. The architecture taking shape is the one I sketched then: Ossie defines the language in which business meaning is written, and the open catalog gives that meaning a governed home next to the tables it describes, discoverable, permissioned, and versioned like any other asset. Definitions stop living in a wiki nobody trusts and start living where the data lives.\n\nThen the modeling and BI layer. The dbt converters are merged, and dbt's MetricFlow can already consume Ossie models directly as an alternative to its native configuration, which makes the promise concrete: a metric defined once in the neutral format, executed by the tool a team already uses. Converters for GoodData and Salesforce sit alongside, with the working groups grinding through the genuinely hard translation questions, time semantics, dialect differences, composability, in public design discussions.\n\nThen the engines. A Spark converter is in review, an expression-language proposal is under discussion in the Iceberg community, and the proposal's long-term vision names collaboration across Spark, Flink, Impala, Iceberg, and Polaris: a future where a semantic query, \"give me monthly active users by region,\" can be interpreted consistently by different engines because the definition and eventually the query specification are standard. That is the roadmap's most ambitious line, a semantic query standard with reference engine implementations, and I will believe it when I see it while being delighted the community is aiming there.\n\nAnd then, of course, the agents, because everything in 2026 ends there. In my agentic standards article I described the emerging stack: MCP as the tool layer, A2A between agents, and a semantic standard as the layer that decides whether agents can be trusted with meaning. Ossie is that layer's candidate, now with neutral governance to match the layers around it. The end-to-end picture writes itself: an agent reaches the lakehouse through a governed MCP surface, the catalog vends it scoped credentials and, alongside the tables, the Ossie-formatted definitions of the metrics it is about to compute. The agent's SQL encodes the company's definition of churn rather than the model's best guess, and its answer matches the CFO's dashboard because both flowed from the same source of truth. Deterministic meaning is the phrase the proposal uses, and it is exactly the missing ingredient every enterprise AI post-mortem has been naming for two years.\n\nLet me make the whole thing concrete with a single metric traveling the pipeline, because abstractions about semantics are exactly what this project exists to end.\n\nA subscription business decides, after one too many Monday meetings, to fix churn. The analytics lead convenes finance, product, and the data team, and they hammer out the definition: churn rate is the count of subscriptions canceled in the period, excluding involuntary payment failures that recover within seven days, divided by subscriptions active at the period start, measured monthly. Today, that hard-won agreement would be enshrined in a slide, and each tool would reimplement it slightly differently within a quarter.\n\nInstead, the team writes it once as an Ossie model: the churn metric with its formula and exclusions, the subscription entity it depends on, the relationship between the billing system's account identifier and the product database's user identifier, the month dimension it is measured over. The model is validated against the schema, checked into version control like code, reviewed like code, and registered in the company's Polaris catalog, where it lives next to the tables it describes, governed by the same access controls.\n\nNow watch it travel. The BI platform reads the model through a converter and builds its dashboards from it, no re-definition, no drift. The dbt project consumes the same model through MetricFlow, so the transformation layer and the dashboard layer are provably computing the same thing. When the data science team's notebook and the finance team's spreadsheet plugin pull churn, they pull the definition, not a rumor of it. When an executive asks the company's AI assistant how churn is trending, the agent retrieves the Ossie definition from the catalog through the same MCP surface it uses for the data, computes with the endorsed formula, and can cite the definition in its answer, including the payment-failure exclusion a guessing model would never have known about.\n\nSix months later, the business decides recovered payment failures should count within fourteen days, not seven. The change is a pull request against one file. It is discussed, approved, versioned, and every consumer, dashboards, transformations, notebooks, agents, follows automatically, with the change history preserved for the auditor who will one day ask why the March number moved. That is the entire pitch of Apache Ossie compressed into one metric's life: define once, govern once, and let every tool and every agent read the same truth.\n\nI am enthusiastic about Ossie, and this series has a rule: enthusiasm travels with the caveats attached.\n\nThe specification is young. Version 0.1 shipped in January, the metrics language is still being formalized, and the hardest semantic problems, time and windowing logic, dialect-specific expressions, composability of models across domains, are exactly the ones the working groups are still designing in the open. Companies evaluating Ossie today should read it as a direction to align with and contribute to, not a finished contract to bet a migration on this quarter. The proposal itself is candid that the roadmap belongs to the community now.\n\nTranslation is genuinely hard. The converter strategy is the right adoption path, and it collides with reality: existing semantic dialects encode subtly different assumptions, and lossless round-tripping between a decade of proprietary formats and a young neutral one will take years of grind. The measure of Ossie's success will be boring converter release notes, not launch announcements, and I mean that as guidance for what to watch.\n\nThe contributor base is concentrated. Three companies dominate core development today, the proposal names this as its top incubation risk, and the ASF's diversity requirement exists precisely to force the issue before graduation. The Polaris precedent says it can be done. The precedent is a plan, not a guarantee, and the new-contributor pipeline, the sprints and first issues and workshops the proposal promises, is where that plan succeeds or fails.\n\nAnd standards can lose. Adjacent efforts, proprietary semantic layers with enormous installed bases, catalog vendors with their own business-semantics ambitions, could fragment the territory Ossie means to unify, and enterprise inertia is the strongest force in software. My grounds for optimism are the roster of competitors already inside the tent and the structural tailwind: the AI agent wave punishes semantic fragmentation more brutally every quarter, and no proprietary format can be the industry-wide answer by definition. But I have watched enough standards efforts to say it plainly: the next eighteen months of community growth will decide this, not the elegance of the specification.\n\nLet me close the analysis with the practical translations, because a project like this touches very different readers differently.\n\nIf you lead data or analytics teams, the strategic move is to start treating your semantic definitions as an exportable asset now, whatever tools you run. Inventory where your metric definitions actually live, get the twenty that matter written down precisely, put them under version control, and watch Ossie's converter coverage for your stack. Every hour spent paying down semantic debt appreciates under any future, and it appreciates fastest under the one where your agents need it.\n\nIf you are a data engineer or analytics engineer, the on-ramp is delightfully concrete: clone apache/ossie, read the core spec, and walk the TPC-DS example, which will teach you the format in an afternoon. If your company runs dbt, the MetricFlow path means you can experiment with consuming a neutral model today. And if you have ever cursed a semantic translation problem between two specific tools, the converters directory is where that scar tissue becomes a valued contribution.\n\nIf you build data tools or work at a vendor, the calculus is the one Iceberg taught: implementing the standard early is how you inherit the ecosystem rather than fight it, and the working groups are open, with the composability and sync API efforts particularly hungry for implementer perspectives.\n\nAnd if you are looking for an open source project to grow with, podlings are the best entry point the Apache world offers. Small enough that individual contributions are visible, young enough that committership is genuinely earnable, and Ossie specifically has committed to the curated-first-issue, onboarding-sprint playbook. The dev list is [dev@ossie.apache.org](mailto:dev@ossie.apache.org), the discussions and Slack are linked from the repository, and the community is at the stage where showing up consistently is the whole secret. I said it about Polaris two years ago and it came true for several people who acted on it. I am saying it again.\n\nThe early questions, answered directly, since a new project generates the same handful everywhere.\n\n**Is Ossie a semantic layer product I install?** No. It is a specification plus converters and tooling: the interchange format that semantic layer products, catalogs, BI tools, and agents read and write. Your tools remain your tools. Ossie is how they agree.\n\n**How is this different from dbt's metrics, or my BI tool's semantic model?** Those are dialects, excellent ones, bound to their tools. Ossie is the neutral format between them, and the tell is that dbt Labs is a core developer building the converters itself. The relationship is Parquet-to-databases, not competitor-to-competitor.\n\n**Why should I trust this will not just serve Snowflake and friends?** Because that is precisely the question ASF incubation exists to answer with receipts rather than assurances: public governance, merit-based committership, and a graduation bar that requires contributor diversity. Watch the archives, not the press releases, and hold it to the standard Polaris was held to.\n\n**Does Ossie handle the actual querying of metrics?** Not yet, by design. Today it standardizes definitions. The roadmap's semantic query standard and reference engines aim at consistent execution across engines, and that is the long game, with the appropriately long timeline.\n\n**What is the relationship to Apache Polaris?** Complementary by design: Ossie defines the format of semantic models, Polaris is building the governed catalog home where they live and are served, and the converter plus the accepted API specification are the handshake. My Polaris article covers the catalog side of the story.\n\n**When can I use it in production?** The honest answer: the specification is v0.1 and incubating, first implementations are real, and the right 2026 posture for most teams is align, experiment, and contribute rather than migrate. If the community executes, that answer changes within a couple of release cycles, and my newsletters will be tracking exactly that.\n\nStep back far enough and the arc of open data infrastructure is one sentence repeated at ascending layers: we standardized the bytes, then the files, then the tables, then the catalogs, and each time, competition moved up and users won. Apache Ossie is that sentence reaching the top of the stack, the layer where data becomes meaning, arriving at exactly the moment AI agents made ungoverned meaning too expensive to tolerate, carried by many of the same people who standardized the layers below, into the same foundation that made those standards trustworthy.\n\nIt is a young podling with a v0.1 specification, a concentrated contributor base, and everything left to prove, and I would not have written five thousand words about it if I thought those caveats were the story. The story is that the last proprietary stronghold in the data stack, the definitions themselves, now has an open, neutral challenger with the right architecture, the right roster, and the right home. Semantic drift has been the quiet tax on every data team's credibility for as long as data teams have existed. For the first time, there is a serious, community-governed plan to end it.\n\nThe project is at github.com/apache/ossie, the site is ossie.apache.org, and the dev list is open to anyone. As for me, I will be covering Ossie's incubation the way I cover Iceberg, Polaris, Arrow, and Parquet, weekly, from the source, in my newsletters.\n\nAnd if you want the deep foundation this whole stack rests on, from table formats through catalogs to the semantics and agents now arriving on top, that is what my books are for. I co-authored Apache Iceberg: The Definitive Guide and Apache Polaris: The Definitive Guide for O'Reilly, with further titles on lakehouse architecture, data engineering, and agentic analytics.\n\nBrowse the full collection of my books on data and AI at [books.alexmerced.com](https://books.alexmerced.com).", "url": "https://wpnews.pro/news/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf", "canonical_source": "https://dev.to/alexmercedcoder/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf-2mio", "published_at": "2026-07-07 18:46:30+00:00", "updated_at": "2026-07-07 18:58:30.544915+00:00", "lang": "en", "topics": ["large-language-models", "ai-agents"], "entities": ["Apache Ossie", "Open Semantic Interchange", "Apache Software Foundation", "Dremio", "Snowflake", "dbt Labs", "Jean-Baptiste Onofré"], "alternates": {"html": "https://wpnews.pro/news/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf", "markdown": "https://wpnews.pro/news/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf.md", "text": "https://wpnews.pro/news/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf.txt", "jsonld": "https://wpnews.pro/news/meet-apache-ossie-the-open-semantic-interchange-finds-its-home-at-the-asf.jsonld"}}