Functionality updates for 4.3.x:
- Ability to export data to a Snowflake table. Among other use cases, this makes it very easy to auto refresh Tableau dashboards from LityxIQ-created datasets.
- Many additional data connectors added.
- Ability to receive notifications based on configuration variable conditions being met.
- Improved processing of datasets marked for automated processing when "Continue upon error" is selected.
- Addition of the Concatenate aggregation option.
- Beta release of the new process flow visualization tool. See https://support.lityxiq.com/515628-Process-Flow-Concepts for more details on getting started.
- Deeper ability to use and set configuration variables to control and automate process flows. This is currently available for dataset objects, but will be made available for any object shortly. See https://support.lityxiq.com/110354-Configuration-Dataset-Settings-Options. Configuration variables impact on a process flow is now also displayed in the Process Flow.
- Notifications (email, text, on-screen) are now available for changes related to configuration variables. A notification can be setup to be received if a configuration variable changes, or based on a condition be met.
Functionality updates for 4.2.x:
- Addition of Percentile-Discrete function for new field aggregations, and improved handling for ranking and distribution functions.
- Alpha version of Process Flow management released to limited users.
- Excel files can be previewed in the preview pane within raw data source settings.
- Queue processing will now happen 25-50% faster.
- Cross-joins (full Cartesian product of two datasets) is now naturally executed when no join keys are selected.
- Improved functionality related to using configuration variables to automatically execute datasets.
- The LityxIQ support site is now integrated into the UI, with help available from every dialog and the main menu.
- New functionality when performing aggregations allows for both cross aggregation variables and individual level variables to be used in the same aggregation.
- Batch loading of external files now operates more easily upon first starting the process.
- Dataset libraries can be fully copied into new libraries.
- Multiple datasets can be selected at once when defining the incoming datasets for a View. This can be done through manually selecting them, or by specifying a dataset name pattern to match.
- Datasets can now use themselves as input. This allows a very simple method for creating an automatic, cumulatively updating dataset.