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How do I build the Meta Model?
Use admin UI to create it. Please refer to its documentation.
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The Meta Model can be built using the Meta Model UI (AdminUI) or by building in a CSV format and uploading the CSV using the AdminUI. Please consult the product documentation.
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Meta models are stored within the Meta folder of the core data lake using the sprngy API. They can be retrieved . AdminUI provides interface for users to save the meta model in the data lake as well as retrieve and edit it. Meta models can be stored and retried using the sprngy API as well.
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Can I know what Meta Model was used for a particular batch process?
Every time an a metamodel is used for any data movement, the definition of the meta model is stored in the track folder (SPRNGYPlatform/Meta/BDL/Fact/Track) along with the batch_id of the data movement process.
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Currently, that is not supported. A child entity can only belong to only one parent entity. If you specify more than one parent, workloads will fail on orphan and nested records processors.
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Special character issue in filter and validation rules - filter records process would fail. The error log will indicate a ‘special character error’. Avoid special characters while defining filter and validation rules. If you are defining meta-model in excel or CSV, export that to an R file using dput function and search for any special characters and if there are any special characters, remove those before saving the metamodel to the core data lake.
No parent is defined in meta model - If no parent information is defined in the meta model i.e parent location and parent attributes, no nested data would be created.
For integersinteger attributes, the default value in meta model should be defined as ‘0' and for numeric attributes, it should be ‘0.01’. For charactersattributes with character data type, the default value should be ‘NOT AVAILABLE’
Impute method for numeric and integer should be mean and for characters, it should be DEFAULT.
meta model column names should be all lower case.
IQM metric match should be ‘YES’ only for integer and numeric columns. This should be set to YES only for integer or numerical columns that are meaningful to data analysis.
IQM codes should be ‘YES' for characters columns. This should be set to YES only for categorical columns that are meaningful to data analysis.
While defining meta model in CSV format, it needs to be ensured that Entity Name should be is exactly as defined in the HDFS folder structureduring ‘Application Configuration’ in AdminUI. The entity name is used to dynamically lookup data lake paths.
EDA Dimensions should be ‘YES’ for character columns. This should be set to YES only for categorical columns that are meaningful to data analysis.
EDA Metrics should be ‘YES’ for integer and numeric columns. This should be set to YES only for integer or numerical columns that are meaningful to data analysis.
EDA iterate by should be the as of the date and only the as of date. This would allow for creating EDA analytics on daily basis.
entity_attribute_nested_key_role should always be set to YES when it is a parent lookup key and it is in use indicator is also set to YES
Entity Attribute Calendar Join Key should only be specified as of the date and no other key.
Each record in meta-model needs to be unique (entity names, object/BI names all need to be unique).
Parent Lookup location should have a relative path instead of an absolute path
IQM Processor should be run with the nesting records processor ON, otherwise, turn IQM off if not Nesting.
Regarding nested attributes
We recommend to keep entity_attribute_compare_key_role, entity_attribute_parent_lookup_key_role as
NOfor as_of_date if the dates are not matching between two entities. If you keepYESthen it would introduce NULLS in nesting for those rows where as_of_date is not matchig matching between the two entity data sets and replace it with impute method defined in the meta model for the given attribute.Moreover, entity_attribute_nested_key_role should be
NOfor the parent entity and it’sYESfor the child entity. If the child entity has another child entity then it should beNOfor that as well. The reason is if as_of_date values are different then it would introduce as_of_date_x and as_of_date_y in the data after nesting.For other matching ID columns between two entities, entity_attribute_compare_key_role, entity_attribute_parent_lookup_key_role should be
YES, with entity_attribute_parent_lookup_location defined to the FDL/Stage of the parent entity. This will nest the column between two entities based on the defined common attribute.
Regarding entity_attibute_calendar_join_key_role
If the data size is small, then don’t set entity_attibute_calendar_join_key_role to YES to any of the attributes. (NOTE: In spark, partitioning is an expensive operation when we try to partition too small or too big dataset)
If the data size is too big (greater than 100GB or something), then the best practice is, don’t try to partition the dataset i.e. don’t set entity_attibute_calendar_join_key_role to YES to any of the attributes.
If the data size is big and If you are setting entity_attibute_calendar_join_key_role to YES to any of the attributes then make sure that column is a date column and the actual data for the date column should be in the format of 'YYYY-MM-DD'. (NOTE: General thumb rule is that there should be one column called as_of_date which would serve all the purposes.). The value of as_of_date should not be ‘NOT AVAILABLE’ when calendar_join_key_role is YES for it.
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