AI/BI Genie for Snowflake

TJ-Leap-Forward — Fri, 14 Nov 2025 17:20:58 GMT

For one of my client, we are building the entire semantic layer for Data (BI) reporting who uses Snowflake. Is there a way to quickly integrate AI/BI Genie on top of Snowflake ?

Re: AI/BI Genie for Snowflake

Louis_Frolio — Fri, 14 Nov 2025 18:00:04 GMT

Hey @TJ-Leap-Forward ,

Yes — you can stand up Databricks AI/BI Genie on top of Snowflake quickly by federating Snowflake into Unity Catalog and then building Genie spaces over those governed datasets, without migrating data out of Snowflake. This works because Genie operates on Unity Catalog–registered data, including foreign (federated) tables and views.

What works and why

Genie spaces use Unity Catalog metadata and author-provided instructions to translate natural language into SQL over your governed datasets, including foreign tables and metric views.
Lakehouse Federation natively supports Snowflake, so you can register Snowflake databases in Unity Catalog and query them from Databricks (read-only), with pushdowns for common operations to Snowflake for performance.
You can choose between two federation modes:
- Query federation (JDBC pushdown to Snowflake compute) for fast, on-demand access.
- Catalog federation (direct reads of Iceberg tables in object storage using Databricks compute) for better cost/performance when your Snowflake data is stored as Iceberg.
  Both options are supported for Snowflake connections in Unity Catalog.
Governance is unified: Unity Catalog handles permissions, lineage, and auditing on federated Snowflake tables, and Genie respects those permissions when answering questions.

Fastest path: 30–60 minute setup

Prereqs
- Workspace is enabled for Unity Catalog; network connectivity to Snowflake; SQL Warehouse Pro/Serverless (2023.40+); Databricks Runtime 13.3 LTS+ for compute.
  7

Create a Snowflake security integration (for OAuth) in Snowflake:

CREATE SECURITY INTEGRATION aibi_uc_oauth
  TYPE = oauth
  ENABLED = TRUE
  OAUTH_CLIENT = custom
  OAUTH_CLIENT_TYPE = 'CONFIDENTIAL'
  OAUTH_REDIRECT_URI = 'https://<your-workspace-url>/login/oauth/snowflake.html'
  OAUTH_ISSUE_REFRESH_TOKENS = TRUE
  OAUTH_REFRESH_TOKEN_VALIDITY = 7776000  -- 90 days
  OAUTH_ENFORCE_PKCE = TRUE;

Create a Unity Catalog connection to Snowflake (UI or SQL):

CREATE CONNECTION sf_conn
TYPE SNOWFLAKE
OPTIONS (
  host 'your-acct.region.cloud.snowflakecomputing.com',
  port '443',
  auth_type 'oauth',
  client_id '<from SYSTEM$SHOW_OAUTH_CLIENT_SECRETS>',
  client_secret '<from SYSTEM$SHOW_OAUTH_CLIENT_SECRETS>',
  oauth_scope 'refresh_token session:role:<ROLE>',
  user 'SNOWFLAKE_USER'
);

Create a foreign catalog that mirrors your Snowflake database:

CREATE FOREIGN CATALOG snowflake_sales
USING CONNECTION sf_conn
OPTIONS (database 'SALES_DB');

Grant access in Unity Catalog (catalog/schema/table as needed) and test a query from Databricks SQL:
```
SELECT * FROM snowflake_sales.public.orders LIMIT 10;
```
Build your Genie space
- Select the federated Snowflake tables/views (and any metric views) as datasets.
- Add example queries, parameters, synonyms, and space instructions to align with your semantic layer.
- Use “Trusted assets” (parameterized examples/functions) to mark vetted answers as trusted.
- Publish to business users (Consumer Access) and monitor with the admin tools.

Performance and architecture options

Query federation is ideal for fast proof-of-concept access; Databricks pushes filters/joins/aggregations down to Snowflake via JDBC to keep latency low.
Catalog federation (Iceberg) lets Databricks read Iceberg tables in your Snowflake catalog directly from cloud object storage, using Databricks compute for better cost/perf. Configure an external location for Iceberg paths; Databricks auto-detects Iceberg tables and falls back to query federation if a table isn’t eligible
For frequently queried Snowflake sources, consider materialized views in Databricks to stabilize concurrency and accelerate cross-source joins, while retaining UC governance.

Important considerations

Read-only: Federated Snowflake tables are read-only from Databricks; writes remain in Snowflake or via pipelines into managed UC tables.
Case sensitivity: Preserve case for Snowflake identifiers by quoting the database/schema/table names where needed.
Limits and sizing: Large result sets can pressure single-stream JDBC reads; design queries/filters accordingly and leverage metric views or materialized views for heavy workloads.
Governance: Unity Catalog enforces permissions and lineage across federated data; Genie uses UC metadata and your semantic annotations to improve answer quality.

Hope this helps, Louis.

Re: AI/BI Genie for Snowflake

TJ-Leap-Forward — Mon, 08 Dec 2025 18:18:00 GMT

Thank you, Louis, for providing all the details — they were extremely helpful. To give you some context, we are an IT consulting and software services company, and we recently became a registered partner. This area is still fairly new to me.

We are currently exploring a Proof of Concept for one of our customers who is building a comprehensive semantic layer with live reporting in Power BI. We would like to demonstrate how AI/BI Genie can add significant value and enhance their capabilities. Could you please guide me on the best approach to get started? Are there any associated costs that we should factor in? Additionally, is there someone else we should connect with for further assistance?

topic Re: AI/BI Genie for Snowflake in Warehousing & Analytics

AI/BI Genie for Snowflake

Re: AI/BI Genie for Snowflake

What works and why

Fastest path: 30–60 minute setup

Performance and architecture options

Important considerations

Re: AI/BI Genie for Snowflake