> ## Documentation Index
> Fetch the complete documentation index at: https://docs.explo.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Building your data model

> Although Explo can sit on top of almost any data model, here are a few tips to ensure better performance and ease of use when connecting to Explo.

### Segment your data by customers

It is very important for your data to be segmented by customers. In most
cases, a customer is your customer, but can also be different entities that
you wish to split your data by.

Ensuring that tables in your database contains a field such as`customer_id`or
`organization_name `can make it much easier and quicker to query and segment
your data.

However, if all your tables do not have a customer field, you can always join
tables in the Explo interface.

<Note>
  Alternatively, Explo also support data models where data for each customer is
  in a separate database. In this case, it is not important to have a customer
  field in each of your tables. Instead you would refer to our API docs to tell
  us where data for each customer is stored.
</Note>

### Transform or process your data to create golden tables

Golden tables are tables that contain the relevant data for a specific business
process that can be easily queried to pull insights or metrics without needing
to join with other tables. These tables should all have a customer field so
Explo can easily segment the data in each table.

These tables should not be aggregated views but rather raw data. Furthermore,
each row should have the same granularity.

As an example, if you are creating a marketplace platform, you may have 3 golden
tables with the following fields:

| Table     | Fields                                                                                                |
| --------- | ----------------------------------------------------------------------------------------------------- |
| Orders    | `order_status`, `SKU`, `quantity`, `shipping_cost`, `price`, `customer_id`, `seller_id`, `order_date` |
| Customers | `customer_name`, `customer_id` ,`email`, `address`, `payment info`                                    |
| Products  | `SKU`, `product_name`, `description`, `seller_id`                                                     |

In the example above, `seller_id` would be used to segment the data for each
customer.

Because you can run aggregations and calculations in Explo, it is best not to
run these aggregations here, so you have the flexibility to do so in Explo.

<Note>
  If you do not have tables that are ready to query, you can leverage a tool such
  as [DBT](https://www.getdbt.com/) or [Airflow](https://airflow.apache.org/) as a
  transform layer to create these tables. This will dramatically improve the
  performance of your dashboards in Explo.
</Note>

### Handle schema complexity upstream

Perform more complex data transformations and calculations in the database or
ETL layer rather than running those calculations in Explo. This will also
dramatically improve load times.