Snowflake Data Transformation can be defined as a set of logical transformations that can be applied to an excel table to extract information from it, to transform it to the required format. The process is very easy to understand and implement and is often implemented by those who are experienced with various excel table formats, such as the Microsoft tables that are in vogue. However, the process has its limitations and potential pitfalls. Therefore, these are discussed below, so that you can keep an eye on them and take remedial measures as soon as they crop up.
One limitation of Snowflake Data Transformation is the inability of the program to alter data that is already present in the table. When the transformation is initiated, it must first be created in the data table before it can add new columns, and so on. If the transformation cannot be initiated properly, then you may find that the existing data is not presented suitably and the resulting chart will not meet the needs of your audience or the specifications of the presentation template that you have used for creating the chart. In such cases, you should consider the option of using the -draw feature, which will allow you to draw and arrange the data in the desired way. In addition to this limitation, Snowflake Data Transformation cannot be applied to an external Rows option, which means that it cannot be used to make any sort of trend line or trend analysis on un-charted tables.
Another limitation of Snowflake Data Transformation is that it cannot handle objects that are of different types and sizes, as well as objects that have different dimensions. If the program can manage these types of objects, then the conversion process can be successful, but the fact remains that it can never handle the conversion of a scalar value into another scalar value, which means that any expressions like a*b cannot be converted into b. Similarly, any expressions like a+b cannot be transformed into a complex expression like a=b+c, as all the types of dimension expressions do not support the combination with other types of dimensions. In addition to these limitations, Snowflake Data Transformation can only handle limited types of objects, such as scalars, doubles, and single values, but not arrays of different dimensions. Get the Snowflake Price here.
When you use the -create clause of SQL script files to create a dimension table via SQL or Oracle database, then you are required to provide a list of columns that are required to produce the final result. In the case of Snowflake Data Transformation, you would be happy to note that such a feature is not available, as such a feature can only be used to convert float values into other dimension names, such as DIM. Such a feature is, however, not implemented by the underlying slicer that you use for creating the final result from the original data. However, if you use a vendor that provides SQL and Oracle integration, then the underlying slicer can easily provide such functionality.
The third option, which can be used to convert Snowflake Data Transformation tables to various other dimension names, is to use the connect method to create the connection between an entity model and a dimension table. This option works well when you need to convert many types of dimensions that are related to one another. For example, you would be able to convert several individual vehicle models into a static model of a single-vehicle type. However, to do so, you would need to use the create star dimension table via SQL or Oracle. It is also possible to connect to multiple dimension tables via the use of the connect method.
The use of the connect method to create the star schema dimension table creates the logical structure that is required in the coordinate transformation algorithm. However, it may fail to provide information about the units of measure and their orientation. So users must use the source indicators to specify the units of measurement and the associated orientation. The convert and the connect methods are useful for converting Snowflake data structures from one format to another. For better understanding of this topic, please click here: https://en.wikipedia.org/wiki/Cloud_storage.
