> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/integrations/connecting-data.md).
# Connecting Data
Connect data to begin training a model
The first step to creating a project in Akkio is to connect data, as data is the fuel for any machine learning model. Akkio is a tabular AI tool, which means you’ll want historical data in a tabular format, such as a CSV, Excel file, [Snowflake](broken://pages/-MVqJrzFpTSTQColDIvV) dataset, or [Salesforce](/akkio-docs/integrations/salesforce.md) dataset. Other options include [Google Sheets](/akkio-docs/integrations/google-sheets.md), [Google BigQuery](/akkio-docs/integrations/google-bigquery.md), [Hubspot](broken://pages/-MlM6r104_H8tv-9dAb8), and [PostgreSQL](/akkio-docs/integrations/postgresql-beta.md).
In machine learning, quality, and quantity are important, so high-quality, large datasets are preferred. “Quality” means having few missing values, properly formatted data, and data indicative of the problem you’re trying to solve. There’s no minimum dataset size for connecting to Akkio, but ideally, your dataset is at least a couple hundred rows, preferably thousands (or millions) of rows.
Crucially, your dataset must be indicative of the problem at hand. For Example, you’ll need a historical customer dataset with a churn column to predict churn.
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