> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/treeverse/dvc/llms.txt
> Use this file to discover all available pages before exploring further.

# Tracking Data

> Learn how to track data files and directories with DVC

## Overview

DVC helps you version control large data files and directories that are too big for Git. When you track data with DVC, it stores the actual data in a cache and creates small `.dvc` files that Git can track.

<Note>
  DVC doesn't store your data in Git. Instead, it creates lightweight `.dvc` files (similar to pointers) that track your data's location and version.
</Note>

## Basic Workflow

<Steps>
  <Step title="Add data files to DVC">
    Use `dvc add` to start tracking a file or directory:

    ```bash theme={null}
    dvc add data/dataset.csv
    ```

    This command:

    * Moves the file to DVC's cache (`.dvc/cache`)
    * Creates a `data/dataset.csv.dvc` file with metadata
    * Adds the data file to `.gitignore` automatically

    <Tip>
      Track entire directories the same way: `dvc add data/images/`
    </Tip>
  </Step>

  <Step title="Commit the .dvc file to Git">
    Now commit the `.dvc` file to version control:

    ```bash theme={null}
    git add data/dataset.csv.dvc data/.gitignore
    git commit -m "Add dataset to DVC"
    ```

    The `.dvc` file is small (typically just a few lines) and safe to commit to Git.
  </Step>

  <Step title="Update your data">
    When your data changes, run `dvc add` again:

    ```bash theme={null}
    dvc add data/dataset.csv
    git add data/dataset.csv.dvc
    git commit -m "Update dataset with new records"
    ```

    DVC automatically creates a new version while preserving the old one in cache.
  </Step>
</Steps>

## Understanding .dvc Files

When you run `dvc add data/dataset.csv`, DVC creates a `data/dataset.csv.dvc` file:

```yaml data/dataset.csv.dvc theme={null}
outs:
- md5: a3d2f7c8b9e1d4f5a6c7b8d9e0f1a2b3
  size: 1048576
  hash: md5
  path: dataset.csv
```

<Info>
  The `.dvc` file contains:

  * **md5**: Hash of the file content (for detecting changes)
  * **size**: File size in bytes
  * **path**: Relative path to the data file
</Info>

## Advanced Options

### Track Multiple Files with Glob Patterns

Use the `--glob` flag to track files matching a pattern:

```bash theme={null}
dvc add --glob 'data/*.csv'
```

### Track Files Without Caching

Use `--no-commit` to create the `.dvc` file without copying data to cache:

```bash theme={null}
dvc add --no-commit large_file.bin
```

<Warning>
  With `--no-commit`, your data isn't protected until you run `dvc commit` or `dvc push`.
</Warning>

### Force Overwrite Existing Tracking

If you need to re-add a file that's already tracked:

```bash theme={null}
dvc add --force data/dataset.csv
```

## Using .dvcignore

Like `.gitignore`, you can create a `.dvcignore` file to exclude files from DVC operations:

```text .dvcignore theme={null}
# Ignore temporary files
*.tmp
*.temp

# Ignore specific directories
/data/cache/
/data/temp/

# Ignore patterns
**/.DS_Store
**/Thumbs.db
```

<Info>
  `.dvcignore` uses the same syntax as `.gitignore`. Patterns are applied to all DVC commands.
</Info>

## Common Commands

<CodeGroup>
  ```bash Add a file theme={null}
  dvc add data/train.csv
  ```

  ```bash Add a directory theme={null}
  dvc add data/raw/
  ```

  ```bash Add with glob pattern theme={null}
  dvc add --glob 'models/*.pkl'
  ```

  ```bash Remove from tracking theme={null}
  dvc remove data/train.csv.dvc
  ```

  ```bash Check status theme={null}
  dvc status data/train.csv.dvc
  ```
</CodeGroup>

## Example Output

When you run `dvc add`, you'll see output like this:

```bash theme={null}
$ dvc add data/dataset.csv

100% Adding...|████████████████████████████████████|1/1 [00:00, 12.34file/s]

To track the changes with git, run:

    git add data/dataset.csv.dvc data/.gitignore

To enable auto staging, run:

    dvc config core.autostage true
```

## Best Practices

<CardGroup cols={2}>
  <Card title="Keep data organized" icon="folder-tree">
    Store data in dedicated directories like `data/raw/`, `data/processed/`, `data/external/`
  </Card>

  <Card title="Track at the right level" icon="bullseye">
    Track directories when you have many related files, individual files when they change independently
  </Card>

  <Card title="Commit .dvc files" icon="git-alt">
    Always commit `.dvc` files to Git so your team can track data versions
  </Card>

  <Card title="Use .dvcignore" icon="ban">
    Exclude temporary or generated files from DVC operations to keep your cache clean
  </Card>
</CardGroup>

## Next Steps

<CardGroup cols={2}>
  <Card title="Remote Storage" icon="cloud" href="/guide/remote-storage">
    Set up remote storage to share data with your team
  </Card>

  <Card title="Building Pipelines" icon="diagram-project" href="/guide/building-pipelines">
    Create reproducible ML pipelines with your tracked data
  </Card>
</CardGroup>