---
title: "Create and Build your data models"
description: "The Lakehouse Manager is the next component you will be using. Using the Lakehouse Manager let us plan out your data structure at the conceptual level"
url: https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/getting-started-organize-data
lang: es
lastUpdated: 2026-09-14
---
> For AI agents: the complete documentation index is available at https://docs.ovhcloud.com/es/llms.txt, the full documentation bundle is available at https://docs.ovhcloud.com/es/llms-full.txt.

# Create and Build your data models

## Objective

The **Lakehouse Manager** is the next component you will be using. It is the component that controls:

- Data warehousing
- Policy Tag Management

Using the Lakehouse Manager let us plan out your data structure at the **conceptual level**. This will be done with the help of the _Tables_ functionality.

![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-home.png)
:::info
This tutorial is meant for **Standard Datasets** using the _Lakehouse Manager Engine_. To know how to follow this same step with **External Datasets** click [here](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/tutorials-external-datasets.md).
:::

## Organize your data in tables

### Create your primary schema

#### Add tables to the data model

Once that your metadata has been extracted, it's time to head to the Tables dashboard. This is where you will **build a unified and queryable view of all your data**.

The empty Tables page should look like this.

![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step1.png)
The _All tables_ tab is where you have access to the entirety of your data. The _New View_ tab allows you to create views of only a part of your data in order to collaborate better in large teams. Since this is a simple tutorial, you should work in the _All tables_ tab.

Now let’s concentrate on creating your primary tables and their attributes.

First, hover your cursor over the blue ➕ **NEW TABLE** button on the right-hand side of the screen. This will reveal the create options :

- Upload a file
- Create from Connectors source
- Create an empty tables

:::info
For the purpose of this tutorial, we shall proceed with **Create from a Connectors source.** To create a table using upload a file refer to the [create tables](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/lakehouse-manager-tables.md#create-a-table) documentation.
:::

![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step2.png)
Once you click on _create from a connectors source_, you will be shown a list of sources from the previous step. Click on source you want to add and proceed further by clicking on _Next_. No need to change the default settings here.

:::info
The options _Build the table, load the table once, and generate Load action for later_ can be disabled and each step can be done individually as well. This can be explored in detail [here](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/lakehouse-manager-tables.md#create-a-table)
:::

Press **Create** and proceed to repeat the same with the second table as well.

![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step3.png)
![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step4.png)
:::info
Note that each time you make a change on the Tables page, your visual configuration is **automatically saved**.
:::

At this stage, your Tables page should look like this.

![Lakehouse Manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step5.png)
:::info
Note that the Platform uses the metadata information captured in the Analyzer step to automatically create tables and assign attribute names & types. The source files provided are good to go, however in a real Project you should use the Analyzer to check the data sources, before dragging and dropping them in the Tables page.
:::

### Create your aggregate table

Now, you will aggregate all important data from the sources (namely rides, dates and temperatures) into one table that will be used in the final application.To create your first _Aggregate_ table, click on the blue ➕ icon. Select _Create an empty table_, and you will be shown a new table configuration. Set a name (_dataset\_history_ for example) and save it.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step6.png)
![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step7.png)
Now, before you start adding attributes to the new empty table you have created you will need to change the view from list to canvas on the bottom right side of the screen to make the next steps easier.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step8.png)
![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step9.png)
Move the attributes below by drag and drop to _dataset\_history_:

|        Original table       | Attributes to drag-and-drop                                              |
| :-------------------------: | ------------------------------------------------------------------------ |
|     **stations\_rides**     | _date_ / _lat_ / _lng_  / _rides_ / _station\_id_ / _station\_name_      |
| **chicago\_calendar\_full** | _month_ / _temperature_ / _week\_day_ / _week\_day\_label_ / _weekend_ / |

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step10.png)
Finally, you will need to create one new attribute to help translate numerical temperature data into understandable categories (cold, hot, ..).

Start by clicking on the ➕ icon that appears at the top of the table _dataset\_history_ when you click on it. You can then create or edit an attribute inside a table.

Define the attribute as such:

|    Attribute name    | Type   | Nature    |
| :------------------: | ------ | --------- |
| **cat\_temperature** | String | Dimension |

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step11.png)
:::info
For the moment, this attribute is not physically specified. This will be done later in another component: the Data Processing Engine.
:::

[<span aria-hidden="true">↪</span> Learn more about Tables](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/lakehouse-manager-tables.md)

### Finalize the build

:::warning
One last quick check! Make sure to **double-check that your data model looks exactly like the one in the screenshots** before moving to the next step. If some attributes are missing you will get stuck in later steps of the tutorial.
:::

Now, click on the _Build_ icon (under the blue ➕ icon) to effectively create/update the tables and attributes in your dataset. This still doesn't load the data into the tables (which will be done in the next article), it simply applies the logical schema to tables and attributes in your underlying dataset.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/lakehouse-step12.png)
:::info
While the visual logical schema of the data is automatically saved, changes to your tables won't be visible in the rest of the Platform as long as they are not built.
:::

The build task for this tutorial shouldn't take more than a few minutes to run. Once it is done, you can move on.

### Add relevant metrics with Virtual Attributes

Before moving on to the physical processing (ETL/ELT) of the data into this model, let's prepare additional metrics for analytics later on. The final application that you are building following this tutorial includes a chart with the **number of rides per day on a given station**:

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/dashboard-final-new.png)
However, you do not have the necessary data to build that chart directly on the primary sources. You will need a metric that gives you the average number of rides per day for a given station and that can be used in queries and dashboards.

But how do you compute it using the Platform? One way to do this is to create a **virtual attribute**. Virtual attributes allow you to calculate SQL formulas that will be **computed on the fly** and won't be stored in the database. They can be used in a query or a chart in your final dashboard.

:::info
Adding or editing virtual attributes does not require rebuilding the schema.
:::

Switch to the **Attributes** page. This page lists all physical and virtual attributes in your data model, and the lineage in your Project.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/attributes-step1.png)
Click on the _New Attribute_ button to create a virtual attribute.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/attributes-step2.png)
In the creation window, make sure to select _Virtual_ as the realm.

![lakehouse manager](/images/public-cloud/data-platform/getting-started/app-init/picts/attributes-step3.png)
Now add these two attributes and their respective SQL code:

|             Attribute name             | SQL                                                                                    |
| :------------------------------------: | -------------------------------------------------------------------------------------- |
| **avg\_rides\_per\_day\_per\_station** | SUM(rides)/COUNT(DISTINCT CONCAT(CAST(date AS VARCHAR), CAST(station\_id AS VARCHAR))) |
|              **yearmonth**             | SUBSTR(CAST(date as VARCHAR),1,7)                                                      |

The **yearmonth** attribute gives you the year and month in the format _yyyy-mm_. You will use it later.

:::info
Notice how you just used two different methods to generate new attributes/metrics from imported data: **adding an attribute to a Aggregate table** and **virtual attributes**.
:::

- Adding a new physical attribute takes up storage and requires you to physically define them in the Data Processing Engine, but this makes them more rigorous as their specifications can then be edited without changing the whole data model.
- Virtual attributes are a quick win but can become hard to manage if you need to modify them when scaling.

It's time to head to the Data Processing Engine to see what's next.

[Prepare your data pipelines with the Data Processing Engine](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/getting-started-prepare-data.md)

## Go further

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