> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# What types of data can Kumo ingest?

Kumo supports the ingestion of various data source types by allowing you to configure connectors for the following:

* AWS S3 (CSV or Parquet files)
* Snowflake - Tables and Views
* Databricks - Tables and Views
* Google Cloud BigQuery - Tables

You also have the option of uploading a local file (CSV or Parquet files less than 1 GB) for ingestion into Kumo. In this case, you can skip connector creation and create a Kumo table directly by selecting Local File Upload.

In terms of data preprocessing, Kumo automatically preprocesses several data types when creating your Kumo table columns, including:

* Numerical: Integers, floats
* Categorical: Boolean or string values typically a single token in length
* Text: String values typically multiple tokens in length, where the actual language content of the value has semantic meaning
* Multi-categorical: Concatenation of multiple categories under a single string representation
* ID: Numerical values used to uniquely identify different entities
* Timestamp: Time/date information (for extracting year/month/date/hour/minute when applicable)
* Embeddings: Consist of lists of floats, all of equal length, and are typically the output of another AI model.
* Column types are automatically detected using heuristics on the distribution of values in each column’s data, and can also be manually configured.