# Overview of a DataBrew project session
<a name="projects.overview"></a>

In a DataBrew project session, you work within an interactive workspace.

![\[DataBrew project interface showing dataset details, sample data, and options to build a recipe.\]](http://docs.aws.amazon.com/databrew/latest/dg/images/project-overview-grid.png)


The left pane shows the current view of your data. The right pane shows the project's transformation recipe, which is currently empty.

In the upper-right corner of the data grid, there are three tabs: `GRID`, `SCHEMA`, and `PROFILE`. Choosing one of these tabs displays a corresponding view in the workspace; thees views are described next.

## Grid view
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Grid view is the default view, where the sample is shown in tabular format. Use the following procedure for a short walkthrough of grid view.

**To take a walkthrough of grid view**

1. Start by viewing the entire space:

   1. Scroll left and right to see all of the columns. 

   1. Scroll up and down to see all of the data values. 

   1. Use the zoom control at the bottom of the workspace to adjust the magnification level of the grid.

1. At upper-right, view how many of the sample's columns are shown and the current number of rows in the sample. 

   To change which columns are shown, choose the **N columns **link (where **N** is the number of columns currently displayed). Choose the columns that you want, and choose** Show selected columns**. 

1. Now you can start experimenting with DataBrew transformations. Try the following:

   1. From the transformation toolbar, choose **Choose Format**, **Change to uppercase**.

   1. For **Source column**, choose a column that contains character data.

   1. Leave the other settings at their defaults.

   1. To see what the transformed data will look like, choose **Preview changes**. Then, to add this transformation to your recipe, choose **Apply**.

   Whenever you apply a data transformation, DataBrew adds it to the working copy of your recipe. This appears at the right side of your workspace. 

1. Try the following:

   1. From the transformation toolbar, choose **Create**, **Based on a function**.

   1. For **Select a function**, choose `SQUARE ROOT`.

   1. For **Source column**, choose a column that contains numeric data.

   1. Leave the other settings at their defaults,.

   1. Choose **Preview changes** to see what the transformed data looks like. Then, to add this transformation to your recipe, choose **Apply**.

1. Collapse the recipe pane at upper right by choosing **RECIPE**. To expand the recipe pane, choose **RECIPE** again.

### Publishing a new version of your recipe
<a name="projects.overview.grid-view.publish-recipe"></a>

As you continue applying transformations, the number of steps in the recipe increases. At any time, you can publish a new version of your recipe. *Publishing* a recipe makes it available elsewhere in DataBrew. By doing this, you can run a recipe job to transform your entire dataset, as opposed to transforming only the project data sample.

Publishing recipes also encourages an incremental, iterative approach to recipe development: You can publish new versions of your recipe as you go, so you can fall back to a "last known good" recipe version if needed.

**To publish a new version of a recipe**
+ In the recipe pane, choose **Publish**. Enter a description for this version of the recipe, and choose **Publish**.

## Schema view
<a name="projects.overview.schema-view"></a>

If you choose the **SCHEMA** tab, the view changes, as shown in the screenshot following.

![\[Schema view showing 5 columns with data types, quality, and value distribution for baby names dataset.\]](http://docs.aws.amazon.com/databrew/latest/dg/images/project-schema-view.png)


In schema view, you can see statistics about the data values in each column.

In the far left column, next to **Show/Hide**, choose any of the data columns. The **Column details** pane appears at right. This pane shows a summary of statistics for the column values. 

You can rename a column by entering a new name for **Column name**.

You can rearrange the column order by dragging and dropping the columns.

## Profile view
<a name="projects.overview.profile-view"></a>

If you choose the **PROFILE** tab, you can see detailed volumetric information about your project. Before doing so, you run a DataBrew job to create the profile.

**To take a walkthrough of profile view**

1. Choose **Create job**, and enter a name for your job.

1. For **Job output**, choose **CSV** for the file type. 

1. Find or create an Amazon S3 bucket and folder in your AWS account where you want the job output from DataBrew to be written:
   + If you already have this Amazon S3 bucket and folder, choose **Browse** and locate them. Make sure that you have write permissions for both.
   + If you don't have this Amazon S3 bucket and folder, create them: 

     1. Open the Amazon S3 console at [https://console.aws.amazon.com/s3/](https://console.aws.amazon.com/s3/).

     1. If you don't have an Amazon S3 bucket, choose **Create bucket**. For **Bucket name**, enter a unique name for your new bucket. Choose **Create bucket**. 

     1. From the list of buckets, choose the one that you want to use.

     1. Choose **Create folder**. For **Folder name**, enter `databrew-output`, and choose **Create folder**.

1. For **Access permissions**, choose an IAM role that allows DataBrew to write to your Amazon S3 output location. 

   For an S3 location owned by your AWS account, you can choose the `AwsGlueDataBrewDataAccessRole` service-managed role. Doing this allows DataBrew to access S3 resources that you own.

1. Leave the other settings at their defaults, and choose **Create and run job**.

1. After the job runs to completion, the workspace displays a graphical summary of the data profile. 

   The **Data profile** overview tab shows a high-level summary of your data's characteristics, as shown in the screenshot following.  
![\[Data profile overview showing dataset summary, including row count, columns, and data types.\]](http://docs.aws.amazon.com/databrew/latest/dg/images/project-profile-view-01.png)

   The **Column statistics** tab shows a column-by-column breakdown of the data values:  
![\[Column statistics tab showing data profile overview, column types, and data quality metrics.\]](http://docs.aws.amazon.com/databrew/latest/dg/images/project-profile-view-02.png)