• 🎊 New features
• 1. Main new features
• a. Drag and drop feature
• Easily create filtered columns
• Easily create charts from grids
• b. Row level security
• 2. Other new features
• a. Mapping table improvement
• b. Top filter improvement

🎊 New features

This release brings a lot of bug fixes and UI improvements.

1. Main new features

a. Drag and drop feature

Easily create filtered columns

This feature makes it easier than ever to create filtered columns independently from the view filters or perform quick formulas. It allows you to drag a column onto another one to create new columns that are filtered according to specific values or date ranges.

• Drag numeric column on text column or the other way around: this will create new columns where the numeric column is filtered by some values of the text column It opens a dialog to select the values to use as filter. Once the user selects the values and clicks on Apply, as many columns as the selected values are created.
• Drag numeric column on date column or the other way around: this will create new columns where the numeric column is filtered by some date ranges of the date column It opens a dialog to select the date ranges to use as filter. Once the user clicks on Apply, as many columns as the selected ranges are created.
• Drag numeric column on another numeric column: 3 quick actions are proposed: Difference, % Change and Ratio. According to the selected option, a new column will be created performing the formula
• Other filtering actions when dragging text column on date column and the other way around. The dragged column is used to filter the column it has been dropped on
• Drag any column on boolean column: the boolean column is used as filter

Example:

• Numeric on date column: Imagine you have a table with sales data for the last 3 years, including a column with the number of sales and another with the sale date. You can drag the "Number of Sales" column onto the "Sale Date" column to create new columns filtered by specific date ranges. For example, you could select to filter the "Number of Sales" column by the date range "Y-1" and "Y-2" and the system would create two new columns with the sales data for 2021 and 2022.
• Numeric on numeric column: Suppose you have a table with data on employee salaries and another column with their bonuses. You can drag the "Salary" column onto the "Bonus" column to perform quick actions such as calculating the difference, percentage change, or ratio between the two columns. This will create new columns that show how the bonus relates to the salary, allowing you to quickly identify trends and patterns in your data.

Example of how to do it in KAWA:

Easily create charts from grids

With this feature simply drag and drop a numeric column onto a text or date column (and vice versa) to automatically generate a new chart. The numeric column will be used as the series data, while the text or date column will be used to group the data.

b. Row level security

With the row level security feature, you can easily restrict access to sensitive data by creating custom rules that map users to the data they have permission to view. This feature works in two simple steps:

Step 1: Create a rule table - First, you'll need to create a rule table that maps your users to the data they are authorized to view. This table should include user IDs and the corresponding data values that they have permission to access.

Step 2: Use the rule table for one or multiple datasources - Once you have created your rule table, you can apply it to any data source to filter rows based on the user who is currently logged in. The rule table will be used to restrict access to the rows that the current user is authorized to view, while hiding any data that they are not authorized to see.

You can find the full documentation for row level security here: ROW Level Security

2. Other new features

a. Mapping table improvement

With our mapping table improvements, you can easily import CSV files to create new mappings for your data. This feature allows you to quickly map values to a specific column and save time in the process.

Here's what you can do:

1. Import a CSV - You can now import a CSV file containing your mapping data directly into our system. Once imported, you can create a new mapping based on the values in the CSV file. This will save you the time and effort of manually creating each mapping from scratch.

The first column of the CSV should contain the same values as the column you are mapping. The second column contains the values to map

2. Ask GPT to enrich your data - With our improved mapping feature, you can also ask our GPT system to quickly enrich your data with relevant information. Our GPT system can analyze your data and suggest new mappings based on the content of your columns. This feature can save you time and help you identify patterns and relationships in your data.

Example:

Let's say you have a dataset containing product names and prices, but some of the product names are misspelled or inconsistent. To analyze the data effectively, you need to group the products together by category.

Import a CSV - The first column of the CSV should contain the product names and the second column the corresponding categories.

Example of how to do it in KAWA:

b. Top filter improvement

The top filter feature allows users to filter a dataset by the top or bottom values in a given column. With our enhancement, users can now filter on any column and select the aggregation method independently from the view settings.