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To extract specific elements from a string in Marketing Cloud Intelligence, such as the second position of a Campaign Name with multiple delimiters, several harmonization methods can be employed:

Calculated Dimensions: These allow for the creation of custom dimensions using expressions or formulas that manipulate existing data. A calculated dimension can be designed to parse and extract segments of a string based on delimiters.

Patterns: This method involves defining a pattern or regex (regular expression) that matches and isolates the desired portion of the string. Patterns are highly effective for strings with complex structures and varying delimiter types.

Mapping Formula: Similar to calculated dimensions, mapping formulas provide a way to apply a transformation or extraction rule to data fields directly within data streams, enabling targeted data extraction like the desired 'Delux' from the Campaign Name.

These methods enable the implementation engineer to accurately segment and extract the needed data from complex string fields efficiently.

In Marketing Cloud Intelligence, when a field is mapped to the main entity key, other related fields should be mapped as attributes of that key if they provide additional descriptors or details. Since 'Room Number' is related to 'Meeting Code', it would be an attribute of the 'Meeting Code' entity, providing additional context to the meetings without serving as a metric or a separate entity key.

Parent-Child configurations in Marketing Cloud Intelligence are used to link different data tables based on shared key values, allowing for the relational organization of data across various streams. While this setup enhances data analysis and reporting by maintaining logical relationships between parent and child tables, it can also introduce performance issues. The complexity increases with the number of relationships and the volume of data, potentially slowing down query processing and data manipulation. Additionally, Parent-Child configurations facilitate the sharing of dimensions and measurements across linked tables, enhancing the data's usability without duplicating it.