QlikView Interview Questions

Qlikview or Qlik pronounced "klik" is an end-to-end platform for data integration, user-driven business intelligence, and conversational analytics. It is the fastest Business Intelligence (BI) and data visualization tool that is fast to deploy and easy to learn.

Here is a list of major functions in Qlikview:

• Aggregation
• Exponential and logarithmic
• Date and time
• Logical function
• Financial
• General Numeric function
• Number Interpretation
• Mapping Function
• Trigonometric and hyperbolic function
• String
• Range

The sheet is used to divide a Qlikview document into different sections. A document will have at least one sheet which is called the Main. If we want more sheets, they can be added too. There are Sheet Properties in Qlikview which can be used to modify the sheets of each document.

The segregation of data into different lists is the most useful tool in Qlikview. There are many features that can give reports on data sources available in the business organization.

Quick storage: All the data that is collected is segregated according to the data type and is stored in the RAM. This ensures quick access to data and also improved user experience.

Configuration of data: It is a tedious task to reconfigure data after discovering the relationship between the data stored. But Qlikview recognizes this relationship as soon as the data is entered.

Data configuration using colors: The data that is entered is configured by Qlikview and it separated by each other with the help of colors. This makes it simple for users to find out the required data.

Finding data associations: When the user searches for data with Qlikview, it undergoes a detailed search. So, the result produced is not only the direct data but the indirect associations too. This allows the user to retrieve all the information at once.

Compression of data: For processing a particular data, any tool will require only the mandatory data bits which are in-memory data sets. Hence, instead of saving the original and very large data, Qlikview compresses this into 10% of the original data size.

Calculation on the fly: The data input is stored in memory with Qlikview. That is why data is compressed. The calculations for analytics are done on the fly which does not require to store pre-aggregated values.

With Qlikview, the analysis is made with the help of sets of different groups. This is applied for aggregation functions. This is used for comparing the sales or production based on the current and previous years.

In QlikView, the QVD flies will store data that can be accessed for future use. The QV application can be optimized when we create QVDs. This gets stored in the RAM which ensures user experience too. We create QVD with the help of Store Command. After creating this you can save the file with a .qvd extension.

The Store command is given below:

STORE<TableName>INTO<Path\FileName.qvd>

Differences between Keep and Join in Qlikview

There are 13 types of charts that can be created with the QlikView application. These are a straight chart, pivot table chart, block chart, grid chart, pie chart, gauge chart, mekko chart, line chart, bar chart, scatter chart, funnel chart, radar chart and combo chart.

QVD is the abbreviation for QlikView Data Files which is exclusive for Qlikview. These can be written and read-only on Qlikview. This will include all the data tables that were either exported or created from Qlikview.

Container Objects can be used to put multiple charts together. It is capable of holding many charts in one box.

In Qlikview, we use a buffer load statement to change a file into a QVD file. So that this can be used in the future. This can also be used to create or modify a QVD file. We can store it in a user-defined location after creating the file using the buffer load statement.

There are seven different loads in Qlikview.

• Resident load: If we have loaded a particular data on the in-memory of Qlikview, then it is called resigning load. This data already resides in the Qlikview memory.
• Preceding load: If a data is being loaded on Qlikview memory using the bits of another existing load statement, that is preceding load.
• Increment load: In increment load, the new values and data are added on an already existing table. We can avoid the step of reloading the table entirely again. The old data is stored in memory with the help of QVD files which is accessed in this case. After retrieving, new data values are added.
• Binary load: If data is added on the RAM through Qlikview files or QVW files, then it is binary load. The QVW files are loaded in the form of binary which is 0 and 1.
• Buffer load: Buffer load is used to create or modify a file in the form of QVD files.
• Inline load: The inline load is not an external file getting loaded on Qlikview. Instead, it is defined and loaded simultaneously on the Qlikview platform. This can be loaded using the Script Editor option. Here we can select Load Inline and then start entering the new data values.

The databases that can be integrated with Qlikview includes Oracle, MySQL, Progress, Informix, MS Access, SQL Server and DB2. The database is back-end and here the data is stored in the form of QVD files. This is because we can retrieve the data from QVD files very easily and also quickly. These files are converted into QVW files on the front end.

To load web files into Qlikview follow the below steps.

• Use the Script Editor and then navigate to the Data Tab.
• Next select Web Files option.
• Now paste the web URL you want to load
• Press Ok.

Often compared to HTML, XML is the abbreviation for Extensive Markup Language. This data is stored with the help of tags and is saved with the .xml extension. For loading such a file in Qlikview, we can use the Edit Script option and click Insert. To load the file, we select Load option in Files. Here we can browse the required file, select and then it gets loaded into Qlikview.

Qlikview is one such platform on which many types of data files can be loaded and used. These data files include web files (.html), XML files (.xml), Excel files (.xls), Did files, CSV files (.csv), Kml files.qvx files and qvd files.

We must first navigate to Script Editor and then select the Database. Then a database connection method should be selected which is either ODBC or OLE DB. After selecting one of them click the Connect button. A list of databases is made available and one Data Source Name is selected. After selecting, the database is connected to the Qlikview document.

To add singles fields in a table to a different table is possible with Qlikview and this is called mapping of tables. This is necessary when we need to add a field and make use of it as a combination of fields from other tables. We use this syntax to map two or more fields from one table to a new table.

mapping ( load statement | select statement )
applymap( 'mapname', expr, [ , defaultexpr ] )

There are two types of the schema used in Qlikview. They are Star Schema and Snowflaking schema. The schema in Qlikview is a dimensional model to store data.

• Star schema: It is a common dimensional model as it uses simple dimensions and facts to organize data. Both fact and dimension are interlinked just like front-end and back-end applications. A fact is an event that takes place when input data is given. The dimension stores the information the fact is associated with. For example, if a user logins a page then the login is the fact and the information entered is the dimension. This scheme is collected for a particular table and is represented in the form of a star. It contains facts and dimensions next to each other. When all of them is put together a start schema is formed.
• Snowflaking schema: It is just an extension to the star schema. As the data increases additional dimensions may have to be added. We can add these new dimensions on the star schema through a relational environment. In this, the dimensions become multiple relational dimensions. When we represent this type of schema, the external extensions from a star schema give a snowflake-like pattern. Hence, this is called Snowflaking schema.