# Normalize date granularity

The Normalize Date granularity step allows to extract date information (eg. *day*, *week*, *year* etc.) in a column intended for aggregation.

### Step parameters

* `Date column` **(string\*)**: the column on which the date granularity must be applied
* `Date granularity to apply` **column(string)\***: Among ( year, quarter number, month, ISO week (monday to monday), week (sunday to sunday) you can choose one or more date granularity to apply.
* `New column` **column(string)\***: the ending date

{% hint style="info" %}
The following granularities are supported:

* `year`: calendar date corresponding to the first day (1st of January) of the year
* `quarter`: calendar date corresponding to the first day of the quarter
* `month`: calendar date corresponding to the first day of the month
* `week`: calendar date corresponding to the first day of the week (sunday)
* `isoWeek`: calendar date corresponding to the first day of the week (monday)
* `day`: calendar date corresponding to the first hour of the day
  {% endhint %}

### Example

**Input**

<figure><img src="/files/QaXfjAs4qvye86P8GJir" alt=""><figcaption><p>Date - Normalize date granularity input</p></figcaption></figure>

**Configuration**

```json
{
  "column": "date",
  "granularity": "year",
  "new_column": "aggregated_date_column",
}

```

**Output**

<figure><img src="/files/vnCgbEA7GTlDKfBL4ZNE" alt=""><figcaption><p>Date - Normalize date granularity output</p></figcaption></figure>


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