Make Snowflake usable by everyone.
AI analytics on the Snowflake data, roles, and metrics you already run the business on.
Run analytics where your data already lives
Warehouse-native, no copies
Golden sends SQL to Snowflake and renders the result. Your warehouse does the work, your logs keep the record, and every answer comes from live data, not an extract that may be out of date.
Snowflake governs access
With OAuth, users query as themselves. Their Snowflake roles, grants, row-level policies, and masks decide what they can see. Golden does not ask your team to maintain a second permission model.
Metrics stay where your team defined them
Golden reads Snowflake Semantic Views, so dimensions, facts, and metrics arrive as certified fields. AI answers, dashboards, and stories use the same definitions your data team already approved.
Golden Analytics × Snowflake
How Golden talks to Snowflake
| Step | What happens |
|---|---|
| 1 · Connect | Use OAuth, key-pair, or PAT. Your admin approves the connection and stays in control. |
| 2 · Prepare | Clean, normalize, and reshape data in Golden. The steps compile into SQL that runs in Snowflake, so no data is copied out. |
| 3 · Query | Golden generates SQL and sends it to Snowflake. No extract or duplicate data store required. |
| 4 · Enforce | Snowflake executes the query under the user's role, so existing grants and policies apply. |
| 5 · Answer | Golden renders the result for analysis, dashboards, and stories. |
Why Golden on Snowflake is different
| Criterion | Traditional BI | Golden Analytics |
|---|---|---|
| Snowflake access | Often relies on extracts, copies, or refresh jobs | Queries Snowflake live. No copied warehouse data |
| Identity | Often uses a shared service account plus app-level permissions | OAuth per-user. Snowflake roles decide access |
| Metrics | Definitions are rebuilt in a separate semantic layer | Reads Snowflake Semantic Views. Metrics stay defined once |
| Architecture | Dashboard-first product with AI added later | AI is built into discovery, prep, analysis, and storytelling |
| Time to first insight | Training and setup slow teams down | Start with a question, chart, dashboard, or story in minutes |
| AI experience | Chat panel separate from the workflow | AI works inside the same workflow users edit and inspect |
| Full workflow | Prep, analysis, and storytelling often live in different tools | Connect, discover, prepare, analyze, and communicate in one place |
Golden connects to the rest of your stack too
Databricks
AI analytics on the lakehouse data, Unity Catalog grants, and metrics you already run the business on. Every statement lands in your Query History.
Alteryx
Alteryx prepares the data. Golden makes it sing. Curated data that took hours to prepare, in front of every analyst and business user in minutes.
Golden × Snowflake
How does Golden connect to Snowflake?
Golden runs directly on top of your Snowflake account and queries it live. When a user asks a question or builds an analysis, Golden generates SQL, sends it to Snowflake, and Snowflake executes the query; Golden then renders the result for analysis, dashboards, and stories. There are no extract jobs, no second warehouse, and no copied data layer — every answer comes from live data in Snowflake.
Does Golden move or copy data out of Snowflake?
No. Golden does not extract, copy, or sync your Snowflake data. The data stays in Snowflake, and Golden returns only the results needed for a given analysis, dashboard, or story. There is no duplicate warehouse to maintain and no stale copy that drifts out of date.
How does Golden respect Snowflake roles, permissions, and governance?
Golden passes each person's Snowflake identity through to the warehouse, so Snowflake governs access. With OAuth, every user queries as themselves, and their Snowflake roles, grants, row-access policies, and dynamic masking decide what they can see. Golden does not maintain a second permission model, so the controls your Snowflake admins already manage stay fully in force.
Where do metric definitions come from when I use Golden on Snowflake?
Golden reads your Snowflake Semantic Views, so dimensions, facts, and metrics arrive as certified fields that are defined once in Snowflake. AI answers, dashboards, and stories all use the same definitions your data team already approved, rather than a separate semantic layer rebuilt inside the BI tool. When a metric changes in Snowflake, Golden reflects that change.
What authentication methods does Golden support for Snowflake?
Golden supports OAuth for per-user identity, key-pair authentication (JWT / RS256), programmatic access tokens (PAT), along with automatic key rotation. Your Snowflake admin approves and runs the security integration, and Golden never self-provisions access. Credentials and tokens are encrypted at rest with per-account KMS envelope encryption, and traffic is pinned to Snowflake domains to prevent token redirects.
Does Golden increase Snowflake costs or change how activity is audited?
Golden's queries run on your Snowflake warehouse, so Snowflake compute does the work and your team keeps cost visibility, query history, and audit trails. Golden identifies itself as a registered partner on each call, so its activity is visible in Snowflake query history. No separate engine performs the computation outside your account.
How is Golden different from traditional BI tools running on Snowflake?
Traditional BI tools often rely on extracts, copies, or refresh jobs, a shared service account, and a semantic layer rebuilt outside the warehouse. Golden queries Snowflake live with no copied data, uses OAuth so Snowflake roles decide access for each user, and reads Snowflake Semantic Views so metrics stay defined once. Golden is also AI-native: AI is built into discovery, preparation, analysis, and storytelling rather than added as a separate chat panel.
Can Golden use Snowflake alongside my other data sources?
Yes. In addition to Snowflake, Golden connects to BigQuery, Redshift, Databricks, Google Sheets, and files such as CSV, Parquet, and JSON. Teams can run one AI analytics workflow across all of these sources, with warehouse queries running in place rather than being duplicated into Golden.
Bring Golden to your Snowflake
Connect Snowflake, keep governance in place, and start building live dashboards on warehouse data.