# Pipelines on unstructured data

As discussed in the overview for [datasets](../../data-integration/datasets/index.md), unstructured data in Foundry is stored as a collection of files in a dataset just like tabular data.

These are some features that work identically between pipelines on structured and unstructured data:

* Pipelines can be made incremental to optimize compute performance.
* You can write unit tests against your pipelines.
* Computing output datasets is done using [builds](../../data-integration/builds/index.md) and [schedules](../scheduling-overview/index.md).
* Foundry's [pipeline security](../security-overview/index.md) features enable robust, end-to-end security guarantees.

Some differences from pipelines on tabular data include:

* Most guidance and example code in documentation focuses on processing dataframes, which are not the input types used for unstructured data.
* You must use the lower-level file system APIs to read and write files in unstructured datasets.
* Because unstructured datasets have no schema, some features focused on validating rows and columns of tabular datasets are unavailable.
* It is possible to use Spark to process unstructured files in parallel, but the APIs are lower-level and more complex than for dataframe processing.

To get started with pipelines on unstructured data, refer to the relevant parts of documentation for Python and Java transforms:

* [Python transforms: Read and write unstructured files](../../transforms-python/unstructured-files/index.md)
* [Java transforms: Read and write unstructured files](../../transforms-java/unstructured-files/index.md)

Once unstructured data has been cleaned and normalized, you can use [Code Workbook](../../code-workbook/overview/index.md) to analyze unstructured datasets and train machine learning models in Python and R. [Learn more about unstructured data access in Code Workbook](../../code-workbook/transforms-unstructured/index.md).