What is data modeling in QlikView?

A data modelling in QlikView is nothing but a pictorial representation of data tables that present in the various database servers, which also include associated relationships to show the data flow process of the entire system or model. This data modeling also helps to define the key fields and data dependency factors that help to perform the normalization and simplification process. The data modelling also a combination of dimensional tables that are linked to represent the fact table and also ends up with a star schema that helps to trade off on the available resources.

QlikView also performs in a good way when the data model is well structured and designed. A good data model also ensures that the quick data process provides accurate results, and evaluates the expressions. Data modeling in QlikView also consists of dimensions and key values within the data fields.

Best techniques used to perform Data modelling in QlikView:

The following are the important key techniques used to perform the data modelling process:

1. Using QVD files to increment data loads:

While performing the data modelling technique, the incremental load is a very common task in the relation to the database servers. It is defined as data loading (which helps to define new or modified records from the various database servers). All the data records will be stored in the QVD file formats.

The following are the important steps that will be considered to perform QVD files increments:

1. First you need to load the data from the database tables (this is considered to be a slow process, but helps to load the limited number of data records).

2. Next you need to load the old data from the QVD files (helps to load the data records in a faster way).

3. Now you need to create the QVD file formats.

4. You need to create the procedure for every table you loaded.

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2. Combining the data tables with Join and keep tables:

We already know that join takes any two different tables and combines them into one table. This process is also known as the “natural join table”. In QlikView, this type of join process can be done by using scripts and also logical data tables’ formats.

Let me explain these two methods in brief:

1. Join:

The easiest way to perform the joins process with the help of the join prefix in the script. The join is an internal table with another name table or previously created tables. The join usually used here is an outer join and used to combine values from the two tables.

For example:

LOAD a, b, c from table1.csv;

JOIN LOAD a, d from table2.csv;

2. Keep:

This is one of the main features of QlikView and helps to associate between any two tables instead of joining them. These keep features also help to reduce the memory space, increase the speed and enormous flexibility. The keep functionality helps to reduce the number of cases where the user needs to make use of explicit joins. In general, the Keep prefixes between two statements are LOAD and SELECT.

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3. Using mapping as an alternative to the joining process:

The Join prefix is a powerful method used to combine several data tables in QlikView. You can also find one disadvantage of using this Join prefix is that while combining larger and big data tables will reduce the performance. To overcome this hurdle, now we have come up with Mapping, this mapping method consists of two columns they are; a comparison field (as an input) and a mapping value field (as an output).

4. working with cross tables:

A cross table is a common type of data table that performs a matrix of values between any two orthogonal header data lists. The crosstable is often preceded by a number of various qualifying columns; you can read them in a straightforward way.

5. Using Generic databases:

A generic database is a kind of table in which all the field names will be stored as field values in one table column, where the field values will be stored in a second. Generic databases are usually used to define the attributes of different objects.

6. Matching intervals to discrete data:

The interval values can be defined using two prefix statements they are; LOAD and SELECT. These statements are used to link the discrete values with the two or more numeric intervals. This is one of the powerful data modelling techniques used in QlikView nowadays.

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7. Creating a data interval from a single date:

Sometimes, when you are working with data modelling, usually the time intervals are not stored with a beginning and end of the time limit. Suppose if you don’t create any data interval, then the date value will be implied by only one field or the change timestamp.

8. Hierarchies:

In data modelling, n-level of hierarchies are used to represent other data fields (geographical and organizational dimensions in the data). These types of hierarchies are usually stored in any adjacent table nodes, for example, each record stored as a node, and the field represents the reference to the parent node.

9. Semantic rules:

Usually, semantic tables are not displayed in the table field viewer.

Below are a few semantic rules:

1. The semantic table should contain three or four columns.

2. The prefix statements like LOAD and SELECT load the semantic table and this table should be preceded by a semantic qualifier.

3. A semantic table either contains a relation between field values of the different fields or field values of the same field. One more important point to be remembered here, a mixture between these two will not be accepted.

10. Data cleansing:

When you load the data from the various tables, the field values will not be named consistently. Data cleansing is required, when there is a lack of consistency, and hinders association.

Conclusion: 

The data modelling in the QlikView blog helps users to create a structured and well-designed data model in QlikView. We have also discussed the top 10 best practices used in data modelling. With the help of data modelling users can understand the data landscape and also enables the organization to analyze and data extraction. Data modelling is considered to be a very important method in many business intelligence tools to perform data analysis and visualization tasks.

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