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

How do I get started?

Accepted Answer

Select "Get started" to create your account in the SemanticFed app, then register your first source and model it in the semantic layer. The scenarios on this site are illustrative — they describe the experience, not 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 covers relational databases, cloud data warehouses, object-storage lakes in open file and table formats, document databases, and SaaS REST/GraphQL APIs. Each connector handles auth, schema introspection, and push-down.

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 questions resolve against the governed model rather than a hallucinated schema.

Question 10

How much does SemanticFed cost?

Accepted Answer

Plans scale from an entry tier for getting started, through professional tiers for growing data teams, to enterprise editions with dedicated tenancy and deeper governance. See the Pricing page for the current plans, or contact us to talk through your needs.

Question 11

Can you help us evaluate SemanticFed on our own sources?

Accepted Answer

Yes. 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 — or select "Get started" and connect your first source yourself.

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. 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. BI tools and notebooks connect to the SQL and GraphQL endpoints, and you connect your own systems through the API. Our team can help set up the built-in connectors with you — see the Services page.

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