---
title: Work with data (Classic)
description: An overview of the tools DataRobot Classic provides for importing, preparing, and managing data for machine learning.
---

# Work with data (Classic) {: #work-with-data-classic }

DataRobot knows that high-quality data is integral to the ML workflow—from importing and cleaning data to transforming and engineering features, from scoring with prediction datasets to deploying on a prediction server—data is critical. DataRobot provides tools to help you seamlessly and securely interact with your data. 

**Import data from various sources, including from external data sources** to minimize data movement and control data governance across your cloud data warehouses and lakes.

![](images/gs-data-1.png)

**Explore patterns and insights** in your data; automate the discovery, testing, and creation of hundreds of valuable new features.

![](images/gs-data-2.png)

## 1: Import data {: #import-data }

Import data into the DataRobot platform from the [AI Catalog](catalog), directly from a [connected data source](data-conn), or as a [local file](import-to-dr).

![](images/overview-data.png)

??? tip "Learn more"
    To learn more about the topics discussed in this section, see:

    - [File size requirements](file-types)
    - [Import data documentation](import-data/index)

## 2: Explore data {: #explore-data }

After importing your data, DataRobot performs [exploratory data analysis](eda-explained), a process that analyzes the datasets, summarizes their main characteristics, and [automatically creates feature transformations](auto-transform) &mdash;the results of which are displayed on the [Data page](histogram) of your project.

Once EDA1 completes, you can use the [Data Quality Assessment](data-quality) to find and address quality issues surfaced in your dataset.

![](images/dq-3.png)

??? tip "Learn more"
    To learn more about the topics discussed in this section, see:

    - [EDA Explained](eda-explained)
    - [View the results of EDA1](histogram)
    - [Data Quality Assessment](data-quality)

## 3: Prepare data {: #prepare-data }

Now that you've explored your dataset and identified areas for improvement, you can: 

Perform [manual feature transformations](feature-transforms). 

![](images/create-derived-feature6.png)

[Prepare your data using Spark SQL](spark).

![](images/catalog-2.png)

[Add secondary datasets and then define those relationships to the primary in Feature Discovery projects](fd-overview).

![](images/safer-1.png)
    
??? tip "Learn more"
    To learn more about the topics discussed in this section, see:

    - [Manual feature transformations](feature-transforms)
    - [Prepare your data with Spark SQL](spark)
    - [Configure a Feature Discovery project](fd-overview)

## Next steps {: #next-steps }

Now that your data is where it needs to be, you're ready to start [modeling](gs-model).