Data Preparation Landing Page
The user can access the Data Grid view of the selected dataset by clicking on the Data Preparation icon. The displayed data in the grid changes based on the number of transforms performed on it.
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The user can access the Data Grid view of the selected dataset by clicking on the Data Preparation icon. The displayed data in the grid changes based on the number of transforms performed on it.
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The Data Grid in the BDB Data Preparation is used for visualizing the data. The data displayed in the grid is a sample from the actual data set or complete data based on the data volume. The grid always shows the first 10 K rows in the dataset.
The grid has a header that displays the column name and column type from the selected dataset.
Each Column Header has a Context Menu icon. By clicking the Context Menu icon, a Context menu gets displayed with some options to be applied on that column.
The following options get displayed while clicking on the Context Menu icon:
Rename column
Hide Column
Delete Column
Delete All Others
Cast to Types
Change to String
Duplicate Columns
Get Character Length
Add Blank Column
Collect Set
It also presents the data type of the column. It is analyzed based on the max match to any data type in the first 10K records. Consider that out of 10000 rows sample, there are 9000 integers and 1000 string values, the selected data type is Integer. The 1000 string rows get detected as invalid rows.
The column header in the Data Grid displays the following information based on the column types:
Columns with Integer values- The Min and Max values
Columns with String values- Total unique count or no. of categories
Columns with Date values- Range of dates including the min-max date
Pagination is implemented for the grid data. The tool displays 100 records on each page.
The Data Grid header displays Data Types. Some of the supported Data Types are as given below:
Integer
Double
String
Date
Timestamp
Long
Boolean
Gender
URL
At the bottom of the Data Preparation page, we now display key metrics to provide valuable insights into the dataset being analyzed. These metrics offer essential contextual information, enabling users to make informed decisions, perform data profiling, and gain a deeper understanding of the dataset being prepared. This includes:
Column Count: The total number of columns in the dataset, allowing users to quickly assess the complexity and scope of the data.
Row Count: The total number of rows in the dataset, providing an overview of the dataset's size and volume.
Data Type Count: The number of distinct data types present in the dataset, enabling users to understand the variety and diversity of data formats and structures.
The Skip Rows functionality will help the user to select the records from the specified index. The user can limit the Data Preparation up to the selected no. of rows by using the Skip Rows option. The skipped rows will be excluded from the original dataset while applying the Data Preparation. The default value for Skip Rows functionality is 0.
A Data Quality Bar appears in the header of the data grid. The Data Quality is indicated through color-coding by clicking on a particular column.
The Data Quality Bar displays three types of data using 3 different colors.
Dark Blue-Valid Data
Orange-Invalid Data
Light Blue- Blank Data​
This option allows the user to instantly hide or show the rows based on their need to derive meaningful insights from the displayed data.
Navigate to the Grid view of any selected Data Preparation.
Click the Show/Hide Columns option given at the bottom of the displayed grid view of the data.
The Show/Hide Columns drawer appears displaying the available columns from the selected Data Preparation.
Select the columns using the given check boxes provided for those columns.
The selected columns will instantly go away from the Data Grid display.
Un-check the check boxes for the same column(s).
The column(s) starts reflecting in the Grid view.
The filter functionality is provided for the user to customise the display by selecting a specific column or row or by selecting a data type from the listed data type options.
Check out the given illustration to understand the Filter functionality.
Navigate to the landing page of the selected Data Preparation.
Click the Filter icon provided on the top right side of the screen.
The Filter dialog window opens.
The user can filter the data display based on the following aspects:
Data Types: Select the data types from the available list based on which you wish the data display to get filtered.
Column: Provide name of a specific column to filter the view by that column. E.g., the given image displays data filtered by the columns that contain the Units word in their title.
Row: Provide name of a specific row to filter the view by that row. E.g., the given-image filters the data view by the rows that contain H19 value.
A notification message gets displayed indicating that the Data Preparation has been saved each time when the user clicks the Back icon to go back. A sample image of the save notification message is given below:
