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Optimized load is much faster and therefore preferable, especially for larger data sets. Optimized loads are possible if no transformations (calculations) are made during the load, and no (almost) filtering is done in a WHERE clause.

The only acceptable filter is using function Exists:

WHERE Exists (Field)

So for example this load can’t be optimized:

Load * From … WHERE Country = ‘US’

But this load can:

Load * From … WHERE EXISTS (Country)

On the other hand, the only time you want the load not to be optimized, is if you load a Mapping table from a QVD file. In this case, Optimized load doesn’t quite work, so you want to make the load non-optimized deliberately.

1st Tier – Raw data is getting loaded and QVD is creating.

2nd Tier – QVD is transform in the business login and requirement of the business and data model is created.

3rd Tier – Reading the all QVD from 2nd tier and make a single QVW or may be any binary load.

QlikView store data in to QVD. QVD have data compression capability. QlikView have better performance compared to other BI because of in memory analytics approach, in built ETL handling capability.

The way in which QlikView’s associative engine works means Nulls don’t really exist in the data it holds, a Null is simply an absence of a data item. For example if a field value is missing we can’t make selection on empty list box. We can replace these null values with our required values (NA, 0, etc…)

For Optimization we can optimized the QVD which is used in the QVW files in the backend. For QVW optimization I believe not use any tools we can manually remove unwanted fields form data model or comment it, remove most complex logics from the load script or create it with the simple way because it will take long time for loading. If using large data base file try to create the incremental load for the QVD or load latest data only.