Which harmonization method can cause query performance issues if many data streams are processed?

The choice of calculated dimensions as the correct answer is based on how this method operates within data processing frameworks. Calculated dimensions, often created via complex formulas or derived attributes, require computation at runtime whenever a query is executed. When multiple data streams are involved, the computational load can significantly increase, leading to degraded query performance.

In scenarios where datasets are large and numerous calculations are needed, the processing time can escalate, especially if these calculations involve aggregations or intricate logic. This increased latency can result in slow response times for queries, affecting the overall efficiency of reporting and data analysis tasks.

Other methods, such as VLOOKUP, patterns, and custom classification, typically have different performance implications. VLOOKUP may rely on straightforward lookups within datasets and can often be optimized. Patterns and custom classifications may also be less resource-intensive compared to calculated dimensions since they generally involve more straightforward processing methods. Thus, while all methods can potentially influence performance, calculated dimensions are particularly prone to issues when scaling with many data streams.