In QlikView 10, container objects give QlikView designers a means to present multiple perspectives on a set of facts in a single, space-saving, easy-to-use object.
This object is not tied to any one viewpoint dimensionality or set of measures or type of object. Containers can simplify the presentation and enhance the analysis of any set of document objects. The list box is the central entity of every QlikView application. Providing users with the right set of list boxes they can use to explore, select, and eliminate values from their analysis is the cornerstone of QlikView document design.
With QlikView 10, designers can now incorporate expressions in list boxes to convey additional business context for each list box value. Designers can use list box expressions to add values, mini-charts, and gauges to list boxes. List box expressions use the same expression syntax that is used throughout the rest of QlikView.
QlikView 10 allows developers using the installed client to switch to a new web view mode. This new mode renders QlikView sheets as they would be seen when deployed through a browser.
The web view mode shows all content including extensions. QlikView 10 introduces a new level of openness for designers and IT professionals.
For IT pros we offer a configurable directory service provider for integration with enterprise directories and user databases. We also deliver a set of new application programming interfaces APIs that facilitate the flow of information and command between QlikView and its environment. With QlikView extensions, designers can create custom visualizations and user interface components for use within QlikView. Extensions can also be used to bring mapping tools, Gantt charts, tag clouds, infographic charts, or any other visualization into QlikView.
Once created and packaged, extensions can be installed and used within any QlikView application. This plug-in mechanism allows extensions to be developed once and reused multiple times.
QlikView supports QVX as both a file format and a message format for communication between a QlikView script and a custom data connector. Besides selections in list-boxes, we can also directly select data in charts.
Let's jump to the Dashboard tab and see how this is done. The Dashboard tab contains a chart labeled Number of Movies , which lists the number of movies by a particular actor. If we wish to select only the top three actors, we can simply drag the pointer to select them in the chart, instead of selecting them from a list-box:.
Because the selection automatically cascades to the rest of the model, this also results in the Actor list-box being updated to reflect the new selection:. Of course, if we want to select only a single value in a chart, we don't necessarily need to lasso it. Instead, we can just click on the data point to select it. For example, clicking on James Stewart leads to only that actor being selected.
While list-boxes and lassoing are both very convenient ways of selecting data, sometimes we may not want to scroll down a big list looking for a value that may or may not be there. This is where the search option comes in handy. For example, we may want to run a search for the actor Al Pacino. To do this, we first activate the corresponding list-box by clicking on it. Next, we simply start typing and the list-box will automatically be updated to show all values that match the search string.
When we've found the actor we're looking for, Al Pacino in this case, we can click on that value to select it:. Sometimes, we may want to select data based on associated values.
For example, we may want to select all of the actors that starred in the movie Forrest Gump. While we could just use the Title list-box, there is also another option: associated search. To use associated search, we click on the chevron on the right-hand side of the search box. This expands the search box and any search term we enter will not only be checked against the Actor list-box, but also against the contents of the entire data model.
When we type in Forrest Gump , the search box will show that there is a movie with that title, as seen in the screenshot below. If we select that movie and click on Return , all actors which star in the movie will be selected. Inevitably, when exploring data in QlikView, there comes a point where we want to save our current selections to be able to return to them later.
This is facilitated by the bookmark option. Bookmarks are used to store a selection for later retrieval. To create a new bookmark, we need to open the Add Bookmark dialog. In the Add Bookmark dialog, seen in the screenshot below, we can add a descriptive name for the bookmark.
Other options allow us to change how the selection is applied as either a new selection or on top of the existing selection and if the view should switch to the sheet that was open at the time of creating the bookmark.
The Info Text allows for a longer description to be entered that can be shown in a pop-up when the bookmark is selected. We can retrieve a bookmark by selecting it from the Bookmarks menu, seen here:. Fortunately, if we end up making a wrong selection, QlikView is very forgiving. Using the Clear , Back , and Forward buttons in the toolbar, we can easily clear the entire selection, go back to what we had in our previous selections, or go forward again.
Just like in our Internet browser, the Back button in QlikView can take us back multiple steps:. Besides filtering data, QlikView also lets us change the information being displayed. We'll see how this is done in the following sections. Cyclic Groups are defined by developers as a list of dimensions that can be switched between users. On the frontend, they are indicated with a circular arrow. For an example of how this works, let's look at the Ratio to Total chart, seen in the following image.
By default, this chart shows movies grouped by duration. If we click on the little downward arrow next to the circular arrow, we will see a list of alternative groupings. Click on Decade to switch to the view to movies grouped by decade. Drill down Groups are defined by the developer as a hierarchical list of dimensions which allows users to drill down to more detailed levels of the data. For example, a very common drill down path is Year Quarter Month Day. On the frontend, drill down groups are indicated with an upward arrow.
Let's go there. This drill down follows the path Director Title Actor. Click on the Director A. Edward Sutherland to drill down to all movies that he directed, shown in the following screenshot. Next, click on Every Day's A Holiday to see which actors starred in that movie. When drilling down, we can always go back to the previous level by clicking on the upward arrow, located at the top of the list-box in this example.
Containers are used to alternate between the display of different objects in the same screen space. We can select the individual objects by selecting the corresponding tab within the container. Our Movies Database example includes a container on the Analysis sheet. The container contains two objects, a chart showing Average length of Movies over time and a table showing the Movie List , shown in the following screenshot.
The chart is shown by default, you can switch to the Movie List by clicking on the corresponding tab at the top of the object. On the time chart, we can switch between Average length of Movies and Movie List by using the tabs at the top of the container object.
After all of the slicing, dicing, drilling, and view-switching we've done, there is still the question on our minds: how can we export our selected data to Excel? Fortunately, QlikView is very flexible when it comes to this, we can simply right-click on any object and choose Send to Excel , or, if it has been enabled by the developer, we can click on the XL icon in an object's header.
Click on the XL icon in the Movie List table to export the list of currently selected movies to Excel. When viewing tables with a large number of rows, QlikView is very good at only rendering those rows that are presently visible on the screen.
When Export values to Excel is selected, all values must be pulled down into an Excel file. For large data sets, this can take a considerable amount of time and may cause QlikView to become unresponsive while it provides the data. Now that we have seen how QlikView works from the point of view of a business user, it is time to get a little more technical. Let's take an in-depth look at the various components that QlikView consists of. One of the key elements of QlikView is that it utilizes an in-memory database.
Compared with a disk-based database, this offers a great advantage when it comes to performance. While disk-access time is measured in milliseconds, RAM access time is measured in nanoseconds, making it many orders of magnitude faster. This is a very valid question. Fortunately, there are two factors which counter this potential problem:.
Coupled with bit operating systems, which can address much larger amounts of RAM than bit systems up to 2 terabyte on Windows R2 , it is feasible and relatively affordable to load huge amounts of data into RAM. Clever compression: QlikView utilizes some sophisticated compression algorithms and some common sense, such as de-duplicating data to significantly reduce the amount of memory that is required to store data. Typically, on-disk data is compressed to 10 percent of its original size when it is loaded into QlikView.
These two factors make it possible to create QlikView applications that contain hundreds of millions—even billions—of records. While the in-memory database is excellent technology, it cannot function on its own. Functionally, data flows through QlikView in the following manner also shown in the following image :. It starts with the source data. There are also many different connectors, ranging from big enterprise applications such as SAP, to social networks such as Twitter.
The data is loaded into QlikView using a load script. This script can be used to extract, transform, and load data into the in-memory data model or to store it to the disk in intermediary data files called QVD files. Data in the in-memory database is stored in an unaggregated format, meaning all aggregations are calculated on the fly.
This simplifies data modeling in QlikView, as there is no need for separate aggregation tables. Selections made by the user automatically cascade throughout the entire data model and these changes are shown by QlikView's presentation engine.
QlikView applications can be presented in multiple clients. The Windows application we used earlier is an example of a client; other similar examples will be covered in the next section. While QlikView deployments within an organization often start with a single or few local installations, they often do not stay that way.
As the use of QlikView expands, keeping track of different versions, dealing with huge amounts of data, reloading and distributing applications, and making sure that only the right people have access to applications becomes increasingly hard when using only the Windows client. Fortunately, QlikTech offers a large range of components which ensure that QlikView can scale from a local deployment on a laptop all the way to an enterprise-wide solution.
These components can be classified into three classes also shown in the following screenshot :. Reload, publish, and distribute content. The Windows application we used earlier to navigate and analyse the data in Movies Database can not only be used to consume content, but it is also the main tool with which to create QlikView documents.
As this book is focused on developers, this will be the main focus for the remaining chapters. When QlikView deployments expand, it becomes impractical to update and distribute files manually. This might work for developer PCs, but it is hardly a cost-effective solution to outfit each user in the organization with large amounts of RAM.
Fortunately, QlikView has three components to mitigate these potential roadblocks to broader adoption:. Read more about what this entails here. Do this to find out: 1.
Log in to QlikView 2. QlikView 12 comes with many key improvements. First and foremost, performance is much better, with shorter response times, faster access to your QlikView documents, along with improved peak performance for higher numbers of concurrent users. This video gives you an overview of QlikView 12 and its improvements. If the NPrinting reporting tool is a part of your QlikView solution and you currently have version The difference between NPrinting 16 and 17 is that the latter has a centralised control panel and a web-based interface, through which the user can generate reports on demand.
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