While QlikView shines in dealing with massive data volumes, sometimes we just do not need to load everything at an atomic level. Data aggregation can, for example, be used in deployments where document segmentation by detail is needed, in which case two documents are created to serve different user groups and analysis needs: one document will have all data with the highest level of detail and another one will have a similar data model but with aggregated (reduced) tables. This way, users are better served by keeping a balance between performance and analysis needs.
In this section, we will implement a document segmentation scenario by aggregating the Flight Data table to create a second document intended for executive users, who only require summary data.
Aggregating the Flight Data table
When aggregating data, the first step is always to define which dimension fields will be left out and which ones will be kept in the summarized table. We should analyze this question by looking at the data from the ground up, that is, by reviewing each dimension from the most granular to the most general. The following list shows the most important dimension fields in the Flight Data table, sorted by granularity:
If we analyze how removing each dimension would individually affect the result of the summarization process, we can find that the most impact would come from removing the Airport dimensions, both Origin and Destination, since those are the ones with the greatest granularity. At the same time, we can say that the Airport dimension does not add much value to the analyses we are looking to deliver in our document, so it's a good choice to leave it out.
Dropping dimensions from the data directly impacts the analyses that can be made in the resulting QlikView document. I Therefore, the decision to leave out certain fields for the sake of I summarization should always be discussed with the end user.
We could remove additional dimensions, for example, Aircraft Type or Carrier, but as we move up the detail ladder to the most general dimensions, those dimensions become more and more important to accomplish different analyses.
We must add that leaving dimensions out should be a thorough decision process, thinking both in terms of analytical requirements and the aggregation rate we can achieve. For example, removing the Country dimension would not result in any substantial aggregation if we keep the State field. Also, what happens if we remove the Airport dimensions but keep Origin City and Destination City? What happens is, not surprisingly, that the table will not be significantly reduced since both fields keep a close relation and their granularity is almost the same (there is only one airport in most cities). Therefore, and for the sake of simplicity, we will also leave out all city, state, and country fields.
Finally, before proceeding, we should keep in mind how many records the original table has, in order to be able to measure how much reduction we achieved in the summarization. In our case, the Flight Data table originally contains 1,256,075 rows.
Moving on to the aggregation process, follow these steps:
Notice how the totals remain the same for both tables.
A smaller table will occupy fewer resources (RAM and CPU) and, therefore, calculations will be faster. If the performance gain attained with data aggregation doesn't mean reducing business value and/or functionality for the end user, then it's a winning approach any day.
The Transformation output
We have loaded the base QVD containing flight data and transformed it by applying aggregations, now what? Well, the next steps would be to store the transformed table, using the store command, into a new QVD file that will reside in the 3.QVDs Transformed folder.
After that, a new data model could be created in the Presentation Layer based on the Airline Operations document, but using the newly aggregated QVD and without the Origin and Destination dimensions. This new QlikView document is intended to serve the users who only need summarized information about the Airline Operations document.
Of course, QlikView offers more aggregation options than summing. The most commonly used options are shown in the following table:
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