New features in 7.4

Connect Data Apps to Data Manager

Data Apps and Data Manager are frequently used together with Data Apps implementing various processes on top of data sets (e.g., data loads, ad-hoc validation, …). We’ve made this kind of usage much easier since you can now link Data Apps to your data sets and show them directly on a toolbar. This allows users to simply click on an icon to run a Data App.

Data Apps linked to the data set behave just like every other app – they can have parameters, interact with any data and produce any kind of output as needed. Once app ends, users will see a notification that will allow them to access the app output (e.g., download a file if any, see the results and so on).

Mapping in Wrangler using Formulas

Starting with this release you can add formulas to your mapping in Wrangler jobs. This allows you to simplify your job – use fewer temporary columns and simply map your data using formula at the end of the job.

At the same time, we’ve reworked the mapping editor user interface to be more comfortable. It is now easier to map columns and use constants. You can even mark column as Not mapped to signify that it has not been forgotten and should remain empty.

Dynamic Preview in Wrangler

Data preview in Wrangler allows you to see exactly what your data looks like as you are building your transformation. We’ve added new kind of preview – a dynamic preview that can show you the results of your step as you are working on it before the step is added to your transformation.

Dynamic preview will show you input as well as output columns of your step together so you can focus on data being impacted by the step instead of having to scroll through your data set to find columns you are working with.

This works very well, especially with formulas where you can easily see which columns are used in the formula and what the formula result is.

Dynamic previews are also available in target mapping editor helping you see your job output right away.

Clover Assistant in Wrangler

Clover Assistant is an AI-powered tool that helps you create your jobs – simply write what you are looking for, and the Assistant will suggest transformation steps to implement your requirements.

In this release we’ve improved the Assistant to understand formulas and new mapping with formulas. It will now be able to generate more complex formulas (including formulas in mapping) but also help you fix syntax errors or even logical errors in your formulas.

Wrangler Jobs as Subgraphs via WranglerJob Component

A common use case for Wrangler is data ingestion where the mapping between potentially many sources and known target layout is implemented in Wrangler by domain experts. This approach allows each team to focus on what they know best – domain experts on data mapping while data engineering team focuses on automation and orchestration.

This kind of use case will now be much easier to implement since WranglerJob component allows you to run Wrangler jobs as subgraphs directly as part of your graphs. You will not need job exports and their conversions to subgraphs – you can now select job to run, and the component will take of the rest.

At the same time, the Wrangler job component will allow you to change settings of your jobs (e.g., data source parameters) or even completely bypass source or target configured in your job for more control in your graph.

Data Manager Improvements

The ability to connect Data Apps to your data sets in Data Manager as described above is the biggest improvement to Data Manager in this release. However, we’ve also implemented many other Data Manager improvements which provide additional user comfort and better user experience as well as additional control over what user can do with data.

New Data Set Permissions

You can now control whether rows can be deleted or inserted in each data set. This allows you to more precisely control what actions your users can take since there are various use cases in which adding or deleting rows may not be allowed.

These permissions are independent of each other and apply to the whole data set for all users regardless of their role.

User Experience Improvements

User experience improvements include better editor for long (even multi-line) values in your data sets, better settings screen, improved column chooser, new context menu and more.

Platform Updates

We’ve expanded the list of deployment stacks supported by CloverDX Server to include Microsoft Windows Server 2025 Standard. Currently supported configurations are therefore the following three stacks and their variations:

The above can be combined with various databases to store the Server configuration database. See more details in our online documentation.

We’ve also updated CloverDX Designer which now requires Java 21 (Eclipse platform no longer supports Java 17). Note that you can still run your Server on Java 17 and Designer on Java 21, just make sure to set Designer’s Java compatibility to 17 to ensure that any Java classes that you may have in your solution are compiled with the correct Java version.

Other Improvements

Additional output ports on Merge and Concatenate components

Merge and Concatenate now allow any number of output ports that will receive a copy of the component’s output. This matches other similar components like SimpleGather or others and allows you to simplify your graph layouts avoiding SimpleCopy components in some cases.

Formatted description in schedules and event listeners

Event listeners and schedules can now have descriptions with formatting. This can help you highlight important pieces of information in description for your schedules or event listeners.

Better handling of rate limits for Data Services

Data Services that have rate limits will now return HTTP 429 (Too Many Requests) error code when called too many times. This will allow callers to adjust their rate of calls to not overload the Server. Additionally, new headers X-Clover-Status-Message, RateLimit-Limit, and RateLimit-Remaining are set to allow the caller to better react and adjust its behavior.