# Data types for Spark

![](/files/-MdvUYcjsd_NKbapidlg)

The handling of data types is explained in [Execute API (v3)](/spark-apis/execute-api/execute-api-v3.md#request_data).

Although Excel is not a [statically typed](https://developer.mozilla.org/en-US/docs/Glossary/Static_typing#:~:text=A%20statically%2Dtyped%20language%20is,not%20indicate%20their%20variable%20types.) language, understanding how Spark processes Excel's *Number Category* is important in ensuring the inputs and outputs are processed correctly.

## *Number Category* in Excel

Please follow these formatting guidelines when preparing an Excel file to be used in Spark.

### Blank values

* For a single cell without a default value, leave the cell blank if possible. Avoid introducing quotations such as `=""`.

### Numbers and text

* To return a number value as a string, consider the following:
  * Set the Format of the cell to the Text category in Excel (see above).
  * Begin cell formulas with `=TEXT(`. This enables custom formatting to be applied to numbers and for them to be returned as a string.
  * Do not use an apostrophe before a number to denote it as a string. This will not prevent a number from being returned as a number unless the cell is defined as a Text category.
  * It is not uncommon to see data validation lists defined as `"0,1,2+"`. See [How to: Prepare an Excel file for Spark](/build-spark-services/how-to-prepare-an-excel-file-for-spark.md#avoid-data-type-clashes).

### Dates and times:

* For dates, use the date format `YYYY-MM-DD`.
* For times, use the time format `hh:mm:ss`.
* For Spark to read a combined date and time field correctly, it must be set to a format that includes date and time. For this type of field, use the Custom category and define the format as either `YYYY-MM-DD hh:mm:ss` or `YYYY-MM-DDThh:mm:ss.sssZ`.


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