Solution
There is a distinct difference between the Data Preparation Add-On and the Analysis Add-On in terms of intended use. The Data Preparation Add-On is primarily specified through an interactive configuration dialog, while the Analysis Add-On primarily acts by executing the associated VBScript, Python or Jupyter Notebook code. You can check the actual behavior of the Data Preparation Add-On and Analysis Add-On in the samples introduced in
this document .
1) Data Preparation Add-On is used for data normalization
Creating a database with a DataFinder instance is often done by loading data from various formats via DataPlugins. T The original data is often stored in various file formats, such as *.csv and *.txt. These data files could be analyzed directly by the Analysis Add-On, but the following two points may cause problems.
- Units, property names and property values read by each DataPlugin can be different
- DataPlugins can take a long time to load the raw data file each time for analysis
Among the above problems, the first point can be solved by modifying the DataPlugin itself, although it takes man-hours and coordination among all the DataPlugins used. As for the second point, it is necessary to re-save the data after reading by DataPlugin as a TDM model. The Data Preparation Add-On is designed to tackle both of these problems head on, standardizing on a data format that can be read at high speed and with harmonized units and properties.
2) Analysis Add-On is used for data analysis.
Define and execute standardized analysis for data standardized by the Data Preparation Add-On. By setting up an Analysis instance according to the processing content for standardized data, you can flexibly execute the desired processing regardless of the original data format. Ideally by the time the Analysis Add-On inspects the data, all the data harmonization has already been tackled either by the DataPlugins or with the Data Preparation Add-On. Alternatively, you can dedicate a separate Analysis Procedure to harmonize the data in a first stage.