Question 1

What is SemanticFed?

Accepted Answer

SemanticFed is a data federation platform. It connects your databases, warehouses, lakes, and APIs as sources, lets you model them once in a governed semantic layer, and runs federated queries across them — so you can ask questions across everything in place, without copying data into yet another store.

Question 2

Is SemanticFed available yet?

Accepted Answer

SemanticFed is pre-launch and currently in waitlist. We are building toward a 2026 debut. Everything on this site describes what the platform is being built to do, written honestly as the target experience — not as a record of real customers, deployments, or benchmark results.

Question 3

Who is SemanticFed for?

Accepted Answer

It is for data teams: analytics engineers and BI leads who want consistent metrics across sources, data engineers who want to retire brittle ETL, governance and security teams who need policy enforced everywhere, and platform teams running data infrastructure for many tenants — plus AI teams that need governed context.

Question 4

What does "data federation" mean?

Accepted Answer

Data federation means querying data where it already lives instead of copying it into a central store first. SemanticFed presents many underlying sources as one logical model and resolves each query across them at runtime, pushing work down to each source so only the rows that matter move.

Question 5

Which sources can I connect?

Accepted Answer

The connector library is being built to cover relational databases (such as PostgreSQL, MySQL), cloud warehouses (such as Snowflake, BigQuery, Redshift), object-storage lakes (Parquet, Iceberg on S3-compatible storage), document databases, and SaaS REST/GraphQL APIs. Each connector handles auth, schema introspection, and push-down. The exact launch connector set will be confirmed closer to release.

Question 6

Does SemanticFed copy or move my data?

Accepted Answer

By default, no. Federated queries run against the data in place. You can optionally enable result caching or materialization for specific hot query paths, with a freshness policy you control — but that is an opt-in optimization, not the default mode.

Question 7

Isn’t federated querying slow?

Accepted Answer

It can be, if an engine naively pulls whole tables across the network. SemanticFed is built around a cost-aware planner that pushes filters, projections, and aggregations down to each source so a warehouse does the heavy work it is good at and only small results cross the wire. For the cases that need millisecond latency, caching and materialization are available.

Question 8

What is the semantic layer?

Accepted Answer

It is a typed model above your sources where entities, dimensions, and metrics are defined once — “active revenue,” “net retention,” “monthly active accounts” become documented, governed objects. Every consumer (BI tools, notebooks, the API, AI agents) resolves the same definition, so the numbers agree by construction.

Question 9

How does SemanticFed help with AI?

Accepted Answer

AI agents pointed at raw tables guess at schemas and ignore access rules. Point them at SemanticFed’s semantic layer instead and they reason over documented metrics with the same access policy and masking applied as for a human — so answers are grounded, reproducible, and inspectable back to a query plan. Natural-language-to-query is on the roadmap, resolved against the governed model rather than a hallucinated schema.

Question 10

How much will SemanticFed cost?

Accepted Answer

Pricing tiers will be confirmed closer to launch. The plan is to scale from a free or entry tier for getting started, through professional tiers for growing data teams, to enterprise editions with dedicated tenancy and deeper governance. Join the waitlist or contact us to talk through your needs.

Question 11

How do I get early access?

Accepted Answer

Join the waitlist. We will notify you the moment access opens. If you are a data or platform team with a specific federation use case, reach out via the contact page and we will work through it with you.

Question 12

What does workspace isolation mean for permissions?

Accepted Answer

Every source, semantic model, policy, and audit log belongs to a workspace, so collaboration, permissions, multi-tenancy, and audit are built in from day one. Identity and authorization are inherited from the Burdenoff platform — SemanticFed does not reinvent auth.

Question 13

How is access controlled across so many sources?

Accepted Answer

You define row filters, column masking, and access rules once in SemanticFed and they are enforced on every federated query, regardless of which underlying source the rows came from. Every query is attributable and logged, and every dataset carries lineage back to its physical sources, so an access review is one consistent thing to read.

Question 14

How is my data protected?

Accepted Answer

SemanticFed runs on the Burdenoff platform with RBAC enforced at the edge, audited actions, multi-tenant isolation, and secrets held in a per-environment key vault. Because federation queries data in place, regulated data can stay inside its residency boundary. See the Security and Privacy pages for detail.

Question 15

Which third parties process data?

Accepted Answer

We publish our subprocessors — the infrastructure, communication, and platform providers that help run the service. The list is on the Subprocessors page; a few launch-related providers are still being finalised and are marked accordingly. Note that your federated source systems remain under your control — SemanticFed queries them, it does not take custody of them.

Question 16

Can I integrate SemanticFed with my existing stack?

Accepted Answer

Yes. SemanticFed is SQL- and GraphQL-first, with programmable surfaces (web, CLI, MCP server, and Node/Python SDKs) and event-driven integrations. Our team can help connect BI tools, notebooks, reverse-ETL, and bespoke sources — see the Services page.

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