> ## Documentation Index > Fetch the complete documentation index at: https://learn.kubit.ai/llms.txt > Use this file to discover all available pages before exploring further. # Product analytics in your warehouse > How Kubit allows you to run various reports over clickstream events in Snowflake, BigQuery, Databricks, ClickHouse or Redshift without copying the data. Kubit runs product analytics on clickstream events that already sit in your cloud data warehouse. Each report compiles to SQL for that warehouse, runs on your compute and returns only the aggregated result. No data silo to secure, and your events stay in your account. This page covers how that works on any supported warehouse. For a worked example on one stack, see the recipe [RudderStack events on ClickHouse](https://learn.kubit.ai/product-analytics/cookbook/rudderstack-clickhouse.md). ## How Kubit reads your events Kubit reads your event tables in place, over a connection you give it. A report goes through four stages: | Stage | What happens | | - | - | | **Model** | You map your tables to fact tables, subjects and fields once, in the Modeler. | | **Compile** | Kubit turns a report (a funnel, say) into SQL in your warehouse's dialect. | | **Run** | The query runs on your warehouse, as the read-only user or role you created for Kubit. | | **Return** | Only aggregated rows come back to Kubit. Event-level rows stay in your warehouse. | Kubit never writes to your event tables. Some features need a small helper database that Kubit owns, such as `KUBIT` in Snowflake or `KUBIT_DB` in ClickHouse. Each warehouse page lists it. ## Ways to get events into your warehouse Kubit supports two ways to give it clickstream events. Both end with Kubit querying a warehouse: | Path | Where events live | Set up with | | - | - | - | | **Your own warehouse** | Your Snowflake, BigQuery, Databricks, ClickHouse or Redshift | The page for your warehouse under [Bring your own warehouse](https://learn.kubit.ai/warehouses/overview.md) | | **CDP destination** | A Kubit-hosted Snowflake account that your CDP loads | The page for your CDP under [Data sources](https://learn.kubit.ai/product-analytics/data-sources/overview.md) | If your CDP already loads your warehouse, use the first path. Your warehouse stays the single source of truth, and you skip a second pipeline and any event replay. **Availability:** Bring Your Own Warehouse is available on selected plans. ## Supported warehouses | Warehouse | Direct connect | Data share | | - | - | - | | [Snowflake](https://learn.kubit.ai/warehouses/snowflake.md) | Yes | Yes (Secure Data Sharing) | | [BigQuery](https://learn.kubit.ai/warehouses/bigquery.md) | Yes | Yes (dataset sharing) | | [Databricks](https://learn.kubit.ai/warehouses/databricks.md) | Yes | Yes (Delta Sharing) | | [ClickHouse](https://learn.kubit.ai/warehouses/clickhouse.md) | Yes | No | | [Redshift](https://learn.kubit.ai/warehouses/redshift.md) | Yes, through VPC access | No | With direct connect, Kubit logs in as a read-only user you create and runs queries on your compute. With a data share, you share tables or views with a Kubit account, and Kubit runs the queries on its side. [Bring your own warehouse](https://learn.kubit.ai/warehouses/overview.md) covers direct connect setup for each warehouse. ## What Kubit needs from your tables Kubit works best on event tables shaped as a star schema: one or more fact tables of events, joined to dimension tables such as users or plans. | Requirement | Why | | - | - | | One row per event, with an event name column | Reports filter and step through events by name | | A user identifier on every event, for signed-in and anonymous users | The User subject counts people, so it needs an ID on every row | | An event time stored as `timestamp` or `date`, not text | Time zone conversion at query time slows every report | | Common properties stored on the event row (app version, platform, country) | Filters and group-bys on inline columns don't need a join | | A date clustering or partition key on large fact tables | Most reports filter by date first | CDP tables mostly fit already. RudderStack and Segment write one table per event plus aggregate `tracks`, `pages` and `screens` tables. Model the aggregate tables first, then add per-event tables for the properties you need. ## Subjects for clickstream data A subject is the identifier a report counts and groups by. A clickstream model needs at least two subjects: | Subject | What it is | Typical column | | - | - | - | | **Event** | One tracked action, page view or screen view | The event's message ID | | **User** | The person who sent the event, signed in or anonymous | The user ID, falling back to the anonymous ID | You can add subjects, such as an account or a device, in the Modeler. You can't delete one once it's created. When you add agent traces later, the Session, Trace and Span subjects join the same model. A user who chats with your agent and then upgrades on the web counts as one User. See [Concepts](https://learn.kubit.ai/agent-observability/concepts.md#subjects) for the trace subjects. ## Analyze the data Once the model exists, the same tools work on any warehouse: | Tool | Use it to | Page | | - | - | - | | **Reports** | Count, convert, retain and follow users across events | [Reports overview](https://learn.kubit.ai/product-analytics/reports/overview.md) | | **Dashboards** | Put reports side by side and refresh them on a schedule | [Dashboards](https://learn.kubit.ai/product-analytics/dashboards.md) | | **Lumos** | Ask questions in plain language and get reports back | [Lumos](https://learn.kubit.ai/product-analytics/lumos.md) | ### MCP prompt examples > "How many users signed up each day last week?" > "Build a funnel from signup to project\_created for the last 30 days, broken down by platform" ## Related * [Bring your own warehouse](https://learn.kubit.ai/warehouses/overview.md): choose direct connect or a data share. * [Data sources](https://learn.kubit.ai/product-analytics/data-sources/overview.md): load events from RudderStack, Segment, Snowplow or mParticle. * [RudderStack events on ClickHouse](https://learn.kubit.ai/product-analytics/cookbook/rudderstack-clickhouse.md): a full setup on one stack. 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