Make the lakehouse usable by everyone.
AI analytics on the lakehouse data, Unity Catalog grants, and metrics you already run the business on.
Run analytics where your data already lives
Lakehouse-native, no copies
Golden sends Spark SQL to your SQL warehouse through the Statement Execution API. Every answer comes from live Delta tables, not an extract that may be out of date.
Unity Catalog governs access
With per-user OAuth, people query as themselves. UC grants, row filters, and column 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 detects Unity Catalog metric views and generates the MEASURE() SQL they expect, so measures arrive as certified fields. AI answers, dashboards, and stories use the same definitions your data team already approved.
Golden Analytics × Databricks
Why Golden on Databricks is different
| Criterion | Traditional BI | Golden Analytics |
|---|---|---|
| Lakehouse access | Often relies on extracts, copies, or refresh jobs | Queries the SQL warehouse live. No copied data |
| Identity | Often uses a shared service principal plus app-level permissions | OAuth per-user. Unity Catalog decides access |
| Metrics | Definitions are rebuilt in a separate semantic layer | Reads Unity Catalog metric views. Metrics stay defined once |
| Governance | Row and column rules re-implemented in the BI tool | Row filters and column masks enforced by the lakehouse |
| 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 |
| Full workflow | Prep, analysis, and storytelling often live in different tools | Connect, discover, prepare, analyze, and communicate in one place |
How Golden talks to Databricks
| Step | What happens |
|---|---|
| 1 · Connect | Use per-user OAuth, a personal access token, or Google federation. Your account admin registers the OAuth app. |
| 2 · Prepare | Clean, normalize, and reshape data in Golden. The steps compile into SQL that runs on your Databricks warehouse, so no data is copied out. |
| 3 · Query | Golden generates Spark SQL and submits it to the Statement Execution API. No duplicate data store required. |
| 4 · Enforce | Databricks runs the statement under the user's identity, so UC grants, row filters, and column masks apply. |
| 5 · Answer | Golden renders the result for analysis, dashboards, and stories. |
Golden connects to the rest of your stack too
Snowflake
AI analytics on the Snowflake data, roles, and metrics you already run the business on. Reads your Semantic Views; OAuth means Snowflake roles decide access.
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 × Databricks
How does Golden connect to Databricks?
Golden connects to your Databricks workspace and queries it live. When a user asks a question or builds an analysis, Golden generates SQL, runs it on your Databricks SQL warehouse, and renders the result for analysis, dashboards, and stories. There are no extract jobs, no separate copy of the data, and no sync to keep current — every answer comes from live lakehouse data.
Does Golden move or copy data out of Databricks?
No. Golden does not extract, copy, or sync your Databricks data. The data stays in the lakehouse under Unity Catalog, and Golden returns only the results needed for a given analysis, dashboard, or story. There is no second data store to maintain and no stale copy that drifts out of date.
How does Golden respect Unity Catalog governance and permissions?
Golden runs every query against data governed by Unity Catalog, so Unity Catalog remains the authority on access. The catalogs, schemas, grants, row filters, and column masks your team already defined continue to apply to what Golden can return. Golden does not maintain a second permission model, so the controls your Databricks admins manage stay in force.
Where do metrics and business context come from when I use Golden on Databricks?
Golden draws business context from Unity Catalog rather than asking teams to re-enter it. Where Unity Catalog carries table and column descriptions, comments, and certified metrics, Golden surfaces them as governed, business-friendly fields in its builder and AI Analyst. People work from the same definitions their data team already approved, rather than raw column names or metrics rebuilt inside the BI tool.
What authentication methods does Golden support for Databricks?
Golden authenticates to Databricks with a personal access token (PAT) or OAuth using Google Workspace identity federation. Your Databricks admin approves the connection, and Golden never self-provisions access. Credentials and tokens are encrypted at rest with per-account KMS envelope encryption, and Golden validates traffic against your Databricks workspace host.
Does Golden increase Databricks costs or change how activity is audited?
Golden's queries run on your Databricks SQL warehouse, so Databricks compute does the work and your team keeps usage visibility, query history, and Unity Catalog audit logs. No separate engine performs the computation outside your workspace, which keeps cost and audit trails where your team already monitors them.
How is Golden different from traditional BI tools running on Databricks?
Traditional BI tools often rely on extracts, refresh jobs, and a governance model rebuilt outside the platform. Golden queries Databricks live with no copied data, honors Unity Catalog so governance stays defined once, and is AI-native, with AI built into discovery, preparation, analysis, and storytelling rather than added as a separate panel. Golden works alongside Databricks as a visual analytics and storytelling front-end on the lakehouse.
How is Golden different from Databricks Genie?
Databricks Genie and Golden address different needs on the same governed lakehouse data. Genie is a conversational interface: users ask questions in natural language, and Genie generates SQL, runs it on Databricks, and returns a summary, table, or chart — ideal for quick, self-service Q&A. Golden gives analysts more analytical control on top of that same data. Rather than being limited to what a chat prompt can express, people build directly with a shelf-based drag-and-drop builder, inspect and edit every query and calculation, choose from 40+ chart types, run deep statistical analysis, and assemble full dashboards and stories. Golden's Slider of Autonomy lets each person decide how much the AI does — from driving manually with full control to letting AI draft the first version — so conversation is one way to work in Golden, not the only one.
Can Golden use Databricks alongside my other data sources?
Yes. In addition to Databricks, Golden connects to Snowflake, BigQuery, Redshift, Google Sheets, and files such as CSV, Parquet, and JSON. Teams can run one AI analytics workflow across all of these sources, with warehouse and lakehouse queries running in place rather than being duplicated into Golden.
Bring Golden to your lakehouse
Connect Databricks, keep Unity Catalog governance in place, and start building live dashboards on lakehouse data.