This release includes new features for replication from Infor CloudSuite Data Fabric, easier way to merge a large Excel sheet with an Excel template, easy way to change datatypes of columns, and the possibility to access certain dataframe or database fields while processing data and more. In addition there are several infrastructure and system improvements.
Key Features
High performance scalable Excel file generation for merging data and Excel templates
For users who wish to automatically produce enterprise grade Excel files with large datasets. The new node Template Excel Writer enables seamless integration of dynamic data into pre-formatted Excel sheets at blazing speeds.
When speed and performance are critical for your business, this high-performance node is designed to handle large datasets efficiently. The node allows for the merger of an Excel sheet (typically fresh data from the ERP system) and a pre-defined template file (with static data, graphs, pivot tables, slicers and so on). The node ingests the data from the Excel sheet to the Excel template producing a structured Excel file.
DataPrep: Change Data type of columns while you prepare data
Many times you wish to change the datatype of a certain column, as you do not always have control over how data is received from your source system, allowing you more flexibility and control during the data transformation process. As an example, some date fields are stored as numbers but you wish to change them to date or date-time format. Certain fields are received as numbers, but you wish to change them to alphanumeric fields and vice versa.
With this new capability, you can now easily change the datatype of any column directly inside the DataPrep node. The system converts the column to a script that performs the conversion requested, with the possibility to further edit the script and do additional conversions.
Scripting: Create New Properties from DataFrame, a Node & Database Utilities
Properties (variables) are extra values used within the workflow, passed between node executions. They can be set as static or dynamic values through scripting. As an example, you can set certain properties through the user input or through certain calculations set during the process of moving data from one node to another.
In this version, we added utilities to set dynamic properties from a dataframe inside the workflow or directly from a direct database query.
The following are the utilities added with examples
DataFrameQueryUtil.queryValue("select count(*) from VisualQuery_2 where t_cuno = " + customerCode);DbUtil.getDbExecutorByName("Sample Database (MySQL)").queryValue("SELECT email from users")In addition, you can choose fields directly using this syntax
VisualQuery_1.row(0).t_nama(Brings column "t_nama" from the first row of the dataframe VisualQuery_1)
DataframeUtil: a utility to fetch data from dataframe
Example for getting the number of rows in Visual_Query_2 for customer <customercode>
DataFrameQueryUtil.queryValue("select count(*) from VisualQuery_2 where t_cuno = " + customercode)Node Input: a utility to fetch specific data from within a node
Example: Getting the value of the first row for column t_namc in node DataPrep_1
DataPrep_1.row(0).t_namcDBUtil: a utility to fetch data from database
Example for getting the number of rows in Visual_Query_2 for user <userid>
DbUtil.getDbExecutorByName("Sample DB").queryValue("SELECT email from users where user = " + userid)VisualQuery Now Supports Reading Database Views
In addition to the existing ability to create new views, the VisualQuery node now also supports reading from existing database views. This enhancement gives users more flexibility by allowing direct querying of both tables and views within the visual interface.
