> ## Documentation Index
> Fetch the complete documentation index at: https://help-empuls.xoxoday.com/llms.txt
> Use this file to discover all available pages before exploring further.
# User data, integrations and connections
> Upload user and sales data, add integrations, and create connections in Compass — walked through with a test user, Carol.
This video walks through user data, sales data, integrations, and the joins (connections) that link them together — using a test user, Carol, so you can see exactly what each screen looks like.
## Steps
User data is your HR data — the fields that uniquely identify each user (like partner code) and the email IDs used to log in to Compass. Under the **Partners** template, each user has entries such as `Carol01` (partner code) and their work email.
Go to **Data and integrations** → **Data sources** to see every table available in Compass. Tables are grouped into three sections:
* **CSV** — tables you defined by manually uploading a CSV
* **Integrated data** — tables populated via native or custom integrations
* **Derived data** — tables derived from an existing Compass table using a group-by function
When you upload data for the first time, Compass asks you to define the table template:
* **Field label** — the column name
* **Field type** — `string`, `float`, `integer`, or `date` (for dates, pick the format)
* Mark one field as the **mandatory unique identifier** for each row
Once the template is applied, your data appears with each field in its defined format. In Carol's row, you can see her partner code alongside her performance fields.
A connection is a join between two tables. Click **New connection**, then:
* Pick a data source (e.g. **Partners**)
* Pick a second table (e.g. **Additive invoice data**)
* Set the join key — for example, `Partners.partner_code = Additive invoice data.partner_code`
Compass supports three join types, covering most use cases:
* **Left join** — all rows from the left table + matching rows from the right
* **Inner join** — only rows that match in both tables
* **Full outer join** — all rows from both tables
Once saved, the joined output becomes a named view (for example, `Active data`). This view combines the user's identifying fields (first name, email, phone, partner code, role) with their performance data — giving you the base needed to compute KPIs and metrics in the next step.