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Additionally, the transform phase addresses data optimisation for

performance, ensuring that only pertinent data subsets are loaded into

Power BI, thereby enhancing the system's efficiency. This optimisation
is visualised in Figure 4.34, which demonstrates the data sorting by

hypertension prevalence rates, a precursor to the data slicing and dicing
that will be performed within Power BI.

Figure 4.34 Top 3 Hypertension-Prevalent Countries in 2010’s query.

Although the information has been pre-processed and cleaned, the

execution of these queries and transformations inside MongoDB is
critical. It not only confirms the dataset's purity, but also structures it

so that it can be used by Power BI's dynamic and strong analytical
capabilities. As a result, the transform phase is an essential component

of the ETL process

iii. Load

Concluding the ETL process, the load phase involves transferring the
transformed data into the analytical tool, Power BI, for further analysis

and visualization. In this case, the'stroke.strokedashboard' collection
from MongoDB was exported as a CSV file, as depicted in Figure 4.35.

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