You can now link or unlink all visualizations while creating a filter or a parameter within a Dashboard in NetSpring - this will save you the time and trouble of linking each visualization individually during creation. To link or unlink visualizations as a whole, you can click the Link All or Unlink All buttons respectively.

We’re excited to announce a brand-new, sleek UX for creating applications and connections in NetSpring! Setting up your apps and connections is now easier and more intuitive than ever. 

NetSpring has introduced several enhancements to Union Dataset: 

- Updating datasets with thousands of tables is now significantly faster, thanks to general performance improvements.

- Modifying names, descriptions, unique keys, and semantics is also quicker.

- You can view and modify Column properties and mappings in a more focused, sleeker modal.

- You can now create union datasets that automatically sync with tables in the warehouse. When you set up a watch on a schema with tables or views, and optionally exclude tables based on name patterns, NetSpring will regularly update the union dataset by adding new tables and removing those that have been deleted.

For a long time we have been maintaining both Visual and Universal Explorations. Over time, Universal Explorations have developed to be a far more robust, and user friendly option for slice and dice analytics, so we have deprecated Visual Explorations. Moving forward Visual Explorations will not longer be creatable in NetSpring. Existing explorations will remain functional. 

We've released a brand new onboarding experience, featuring a checklist of tasks for users to quickly set up their workspace. Each item on the list includes a brief video tutorial on how to complete the action, along with links to feature setup and documentation. As users complete each task, the items are checked off the list. 

NetSpring has introduced a new type of dataset called Column Actor. Typically, an actor is represented by a dataset that corresponds to a dimension table in the data warehouse. However, if there is no corresponding dimension table and only an identifier column in an event table such as USER_ID, you can create a Column Actor dataset. Instead of directly mapping to a table in the warehouse, this dataset is defined by its relationship to identifiers in other tables.

NetSpring now supports full charting capabilities for SQL explorations!  Previously only Tables were supported, but now you have access to the same extensive chart customization capabilities as our self-service templates. Whether you want self-service insights, or just to get deep into the data yourself and craft your SQL query, NetSpring can handle the job.

Blocks in NetSpring have an all-new look and feel! This change improves all blocks for Cohorts, Derived Columns, and Metrics.

NetSpring now has a new chart type called Pivot Chart that allows you to get a multi-dimensional view of your table data. This chart type is available to choose in all exploration templates.

NetSpring has significantly increased its CSV download capabilities, raising the limit from 5,000 rows to 1 million rows for all visualizations. This substantial increase allows users to export much larger datasets, facilitating more comprehensive data analysis and reporting.