> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdv/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/data-preparation.md).

# Data Preparation

Multi table data is present in multiple tables that each have rows and columns. The tables are connected to each other through foreign and primary key references.

<figure><img src="/files/m1Iuq2Jqjk4o6YoMDk3T" alt=""><figcaption><p>This example of a multi table dataset has a table for hotels and a table for their guests. Each guest can have multiple stays at multiple hotels.</p></figcaption></figure>

Before you begin creating synthetic data, it's important to have your data ready in the right format:

1. **Data**, a dictionary that maps every table name to a pandas DataFrame object containing the actual data
2. **Metadata**, a MultiTableMetadata object that describes your table. It includes the data types in each column, keys and the connections between tables.

<details>

<summary>Click to see the metadata</summary>

```python
{
    "METADATA_SPEC_VERSION": "MULTI_TABLE_V1",
    "tables": {
        "guests": {
          "primary_key": "guest_email",
          "alternate_keys": ["credit_card_number"],
          "columns": {
            "guest_email": { "sdtype": "email", "pii": True },
            "hotel_id": { "sdtype": "id", "regex_format": "HID_[0-9]{3}" },
            "has_rewards": { "sdtype": "boolean" },
            "room_type": { "sdtype": "categorical" },
            "amenities_fee": { "sdtype": "numerical" },
            "checkin_date": { "sdtype": "datetime", "datetime_format":  "%d %b %Y"},
            "checkout_date": { "sdtype": "datetime", "datetime_format": "%d %b %Y"},
            "room_rate": { "sdtype": "numerical" },
            "billing_address": { "sdtype": "address", "pii": True},
            "credit_card_number": { "sdtype": "credit_card_number", "pii": True}
          }
        },
        "hotels": {
            "primary_key": "hotel_id",
            "columns": {
                "hotel_id": { "sdtype": "id", "regex_format": "HID_[0-9]{3}" },
                "city": { "sdtype": "categorical" },
                "state": { "sdtype": "categorical" },
                "rating": { "sdtype": "numerical" },
                "classification": { "sdtype": "categorical" }
            }
        }
    },
    "relationships": [{
        "parent_table_name": "hotels",
        "parent_primary_key": "hotel_id",
        "child_table_name": "guests",
        "child_foreign_key": "hotel_id"
    }]
}
```

</details>

### Learn More

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><a href="/pages/hSfxZiScNZTAFoZpM6ph"><strong>Loading Data</strong></a></td><td>Get started with a demo dataset or load your own data.</td><td><a href="/pages/hSfxZiScNZTAFoZpM6ph">/pages/hSfxZiScNZTAFoZpM6ph</a></td></tr><tr><td><a href="/pages/70dzL3ZTDgj8HBQH5gTc"><strong>Creating Metadata</strong></a></td><td>Create an object to describe the different columns in your data. Save it for future use.</td><td><a href="/pages/70dzL3ZTDgj8HBQH5gTc">/pages/70dzL3ZTDgj8HBQH5gTc</a></td></tr></tbody></table>

## Multi Table Schemas

{% hint style="info" %}
**What kinds of multi table schemas are compatible with the SDV?** The SDV can be used to model many different types of multi table dataset schemas as long as they meet the criteria below.

1. **All the tables should be connected in some way.** If you have disjoint sets of tables, you can model each set separately.
2. **There should be no cyclical dependencies.** For eg, a table cannot refer to itself. Or if table A refers to table B, then table B cannot refer back to table A.
3. **There should be no missing references** (aka orphan rows). If a table A refers to table B, then every reference must be found. Note that it is ok if a parent row has no children.
   {% endhint %}


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