QlikView vs Tableau: What's the Best Choice for a Business Intelligence Suite in 2019?
Business Intelligence software rivals QlikView and Tableau have been battling for pole position for over fifteen years. Check out our comparison study to find out but which now has the edge for the modern enterprise.
- The Forces Changing the BI Software Space
- QlikView and Tableau Go to War, Eventually
- QlikView Catches Up with Tableau the Hard Way
- QlikView vs Tableau: Relative Estimated Positions in Market Reach and Brand Awareness
- Requirements and Operating Environments
- Database Support
- Interface Usability
- Visualizations and Data Discovery
- Tableau vs QlikView: Pricing
It's an interesting period to be deciding on a business intelligence (BI) suite. At the time of writing, the market is undergoing a round of buy-ups so significant and so close together as to represent a distinct phase of new consolidation in this sector. This leaves existing subscribers, potential clients and BI consulting companies reconsidering the merits of the leading market solutions.
On June 6, 2019, search and AI giant Google revealed initial details of its acquisition of US-based BI framework Looker. Four days later, embedded analytics platform Logi Analytics announced that it is purchasing former rival ZoomData. That same day, CRM and enterprise solutions giant Salesforce pledged to acquire leading BI suite Tableau.
The Forces Changing the BI Software Space
Some have theorized that this shake-up of an over-populated market was the inevitable result of the success of Microsoft's Power BI suite. Gartner has consistently rated Microsoft's analytics offering as the market leader in its respective Magic Quadrant since the product's official launch in 2015. Therefore, it's possible that the 'independent' players in the BI arena needed to scale up in order to stay viable against such a committed and influential industry player.
Others believe that the consolidation is the result of convergence, as the heightened interest in enterprise-level big data applications coincides with the current breakneck pace of development in machine learning and artificial intelligence—promising more powerful insights to whichever suite can best leverage these technologies in the most approachable, performant and economically viable way.
In any case, since the BI space is changing so much, we thought to take a fresh look at Tableau vs QlikView, the two suites which have been considered 'standard' choices over the last 15-25 years.
QlikView and Tableau Go to War, Eventually
Founded in 1993 in Sweden, QlikView was one of the earliest business intelligence products to gain traction. Its dominant position throughout the 1990s and early 2000s gave it an opportunity to significantly define the market, as well as the paradigms and expectations of BI software.
However, QlikView's long tenure without a comparable challenger led to an ever-growing amount of technical debt in the desktop product and the models which defined it. By the time California-based suite Tableau emerged to challenge the incumbent champion with a new emphasis on visualization and ease of use, it was practically impossible for the latter’s founder Qlik to retrace its steps and re-tool its framework to parry the new rival.
QlikView had grown up with the internet, sharing its early focus on a granular developer-oriented workflow, with menu-driven usage models that were beginning to look dated as the new century set in. Like the early internet, it was powerful and configurable; but it was also a little opaque in comparison to the drag-and-drop user experience that Tableau was offering.
QlikView Catches Up with Tableau the Hard Way
In 2014, Qlik finally decided to address many of the product's shortcomings, relative to Tableau's glossy and integrated solution, by launching a parallel product to its venerable desktop client. The cloud-based QlikSense runs over the same data architecture as the desktop client QlikView, but matches Tableau's focus on transparent data discovery, dazzling and innovative visualizations, easy view creation, and full compatibility with mobile devices.
QlikSense was never intended as a successor to QlikView. It was conceived as an equivalent product for companies looking to analyze and discover existing data rather than invent complex and challenging new data-flow structures for reporting and analytics. For those requirements, QlikView remains an able and powerful resource. If anything, QlikView's more accessible tooling could be argued to suit better to today's emerging machine learning and big data developer environments, where GUIs and interconnections are often explicit, rudimentary, or experimental.
However, Qlik's product-base splits Tableau's equivalent capabilities over two products, which is an additional consideration in choosing between the contenders. Nonetheless, since we need to compare the most essential and versatile aspects of each offering, we will consider Qlik's original and more configurable product in relation to Tableau.
QlikView vs Tableau: Relative Estimated Positions in Market Reach and Brand Awareness
At the time of writing, qlik.com stands at 19,413 in Alexa's ranking for global engagement, significantly behind tableau.com, which occupies position 2,367. Qlik estimates its customer base at 48,000 vs. Tableau's approximately 86,000 customers.
In terms of Gartner's Magic Quadrant for Analytics and Business Intelligence Platforms, as of this time Qlik has shared the top placement for nine straight years. Its position and stable-mates have varied, though it has consistently appeared below Tableau and Microsoft in that top catchment area. The younger Tableau can currently claim seven successive years in the same Magic Quadrant.
There is also a distinct east-west split in these trends, with Tableau at 83% in North America and Qlik dominating the Russian BI market at 61%. In neighboring China, however, Tableau dominates at 76%.
Requirements and Operating Environments
Qlik is attempting to transition to platform-agnostic network environments via its more modern (but restricted) QlikSense product. Therefore, it foregoes the extra development effort needed to support an OSX or Linux client. Currently QlikView supports 64-bit versions of Microsoft Windows, including 7, 8.1, 10, Server 2008 R2, Server 2012, Server 2012 R2, Server 2016, and Server 2019.
QlikView's local install requires 4 GB of RAM and 300 MB of disk space.
Though virtualization is not banned for QlikView, it's neither directly supported nor encouraged.
Tableau offers desktop clients for 64-bit Microsoft Windows from version7 upwards, as well as Microsoft Server 2008 R2 or newer. Additionally, there is an OSX client, which requires a minimum of OSX 10.13 running on an iMac or MacBook from 2009 or later.
The company specifies a minimum 2 GB RAM for a Windows install, and 1.5 GB of disk space across both platforms.
Official support is provided for a number of virtual environments, including Parallels, Citrix, Microsoft Hyper-V and Azure, Amazon EC2 and VMware.
QlikView provides a dedicated iOS client that can also work offline if used on an iPad (with certain pre-conditions). For general mobile availability, Qlik has developed an Ajax-driven framework that makes accessing your information via a compatible mobile browser the best current solution. The mobile client is actually just a dedicated browser-wrapper.
After discontinuing its early Android mobile client some years ago, QlikView is currently piloting a new beta for Google's OS. Apparently, it is also a browser wrapper for an HTML5-oriented view of user data, drawn through QlikView's AccessPoint document listing service.
Tableau provides dedicated iOS and Android apps. The apps can access data either via Tableau Server or the cloud-based Tableau Online platform. Layout views can be configured directly for end-user mobile devices.
All the dedicated apps listed above can be controlled and configured via Mobile Device Management (MDM) solutions.
QlikView-licensed ODBC drivers are available for the following:
- Amazon Redshift
- Apache Hive
- Azure SQL
- Cloudera Impala
- Google BigQuery
- IBM DB2
- Microsoft SQL Server
- MySQL Enterprise
- Sybase ASE
Beta ODBC drivers are also available for Apache Drill, Phoenix, Spark, and MongoDB.
Tableau supports all of the above and lists an extra 57 officially supported database schemas. Additionally, its Web Data Connector SDK can facilitate the mapping and resourcing of other network assets. Tableau can also support additional local or network sources via ODBC mapping.
Like the Qlik ecosystem, further open-source and commercial connectors can make diverse alternative datasets available to the client (for instance, the MemSQL Named Connector for real-time analytics). Tableau also supports the rationalization of PDF files as data sources—a function that in QlikView will require some third-party parsing.
The QlikView interface retains the retro stylings of Windows XP, circa 2001, bucking the modern trend for minimalist user interfaces and context-based GUI practices. Most of its dialogues are modal pop-ups, and its creation processes rather linear.
Whilst lacking the unfortunate and dated stylings of QlikView, Tableau's own interface is similarly menu-dependent.
Tableau's workflow is less cluttered, and more contextual and responsive. Its increased emphasis on drag-and-drop functionality can make for a flatter learning curve in respect to QlikView.
However, we should evaluate BI software less superficially, in the light of 'back-office' functionality intended to support useful and clearly-presented data insights. The 'end users' are frequently those that we are trying to persuade, rather than those who are building the processes that make the data explicable.
Visualizations and Data Discovery
Both suites are well-equipped to output a range of stunning visualization types, including heat maps, graphs, pivot tables, bar/line/circular charts, tables, block, funnel, pie and scatter charts. As we’ll see shortly (in 'Performance' below), Tableau is more capable of aggregating and reviewing a disparate array of data sources without needing a great deal of pre-planning. At the same time, QlikView's approach means that some visualization sets might be more sensitive and immediately responsive to interaction than in Tableau.
Since Tableau emerged when geomapping was becoming a popular technology, its support for this aspect of data analysis is still ahead of QlikView's. Tableau has a native extension to handle map visualizations, and supports a wide range of geo-data formats.
QlikView's equivalent functionality was initially provided by third-party applications such as GeoQlik. Even now, in terms of native implementation, setting up a geodata layer in a Qlik data workflow is a less intuitive process.
QlikView's native Extract, Transform, and Load (ETL) workflow makes it relatively easy to develop proprietary QlikView Data (QVD) sources, or a more generalized data warehouse. The Tableau Prep Builder, on the other hand, can only provide part of this functionality.
In general, QlikView is expecting more pre-planned effort in the backend of your visualization than Tableau, requiring more development time and resources to achieve similar stylings to its rival.
Associative Selection and In-memory Compilation in QlikView
QlikView's Associative Selection model, which provides contextual feedback in the interface based on user input, is generally praised as one example of how the software's 'in-memory' technology can speed up interactive performance, compared to Tableau.
However, this touches on differences between the two products which are conceptual in nature: QlikView was conceived as an architectural toolkit for data visualization, and Tableau as a data discovery framework, which necessarily must trade off some flexibility against latency. Indeed, concerns around 'ease of discoverability' are one of the core challenges that Qlik has chosen to address in the QlikSense platform, rather than in its older desktop client.
Therefore, the speed you're enjoying via QlikView's associative filters has to an extent been paid for upfront in design/development time, or at least in load time. In one sense, it's a 'compiled' feature with a measurable cost in terms of implementation and inflexibility.
The other factor fueling QlikView's responsiveness is its increased RAM requirements (relative to Tableau) as the data increases, along with the number of joins and relationships necessary to develop and visualize insights.
OLAP Data Cubes
In its Windows desktop client, Tableau can natively utilize multidimensional or OLAP data sources, known as cubes, to speed up data exploration. QlikView can access cubes via the SAP OLAP Connector. Cubes are pre-compiled data groupings in which decisions about the available granularity of the information have already been made in order to increase performance. This pre-mapped approach can affect the user's ability to explore the data ad hoc.
There is no apparent prospect of OSX support for cubes in Tableau at the time of writing.
QlikView desktop makes use of the standard security features of the Windows NTFS file system. Access to the QlikView Enterprise Management Console (QEMC) is handled with Integrated Windows Authentication (IWA), ticketing, and HTTP headers via Windows User Groups.
Alternatively, one can use Qlik's Access Control List, the Document Metadata Service (DMS). Users who require access outside of a Windows environment can be granted it from the QlikView Management Console (QMC) or through QlikView Publisher distribution tasks, where applicable.
Tableau (and Tableau Server)
Tableau also features support for Windows' native security, including Active Directory credentials. In certain circumstances, using these OS-specific methods can limit the configurability and granularity of the data access, as well as the level of cache synchronization an end user experiences, compared with other users who are viewing the same data at the same time via different authentication methods.
Due to its greater cross-platform accessibility, Tableau has a wider range of security initiatives, methods, protocols and support for third-party authentication systems. Tableau Server is arguably a better and more flexible solution for shared group environments that span a range of platforms and devices. Now available for Linux as well as Windows, Tableau Server has its own Local Authentication facility that's capable of negotiating access rights across a number of differing authentication protocols, ensuring parity of access among users.
Tableau vs QlikView: Pricing
In line with the cryptic standards and practices of enterprise licensing, you'll need to build a detailed and very well-researched use case to understand what the costs are likely to be for either offering, as well as which products you're going to need, and (in some cases) the number of required seats per product.
Qlik's products are already fragmented between QlikView and QlikSense, and between them there are many deeper layers of pricing granularity depending on features required, the number of users, and the type of license. Licensing agreements generally range from 1 to 3 years' commitment.
However, the QlikView Publisher, Information Access Server, and Extranet server all come with their own range of price-points, depending on your needs. The recently implemented Unified License system can help users cherry-pick the strengths of QlikView and QlikSense. You might need professional advice or direct consultation with the company or a reseller.
QlikView has a free personal edition for recent Windows X64 operating systems. However, this can only open documents created by the user's own version of the software, as well as educational files that QlikView provides in its help section. Therefore, it may be of limited use for evaluating QlikView's broader capabilities.
Tableau switched from a perpetual licensing model to a SaaS model in 2017.
Tableau does offer a more leisurely stroll through the suite's functionality in the form of Tableau Public. As the name suggests, any data you run through the system will be publicly available, and data is limited to 15,000,000 rows per workbook.
In a way, this is an unfair comparison, since we are comparing the relatively hermetic Tableau with a product that has already split in two for lack of foresight. But what can we do? Qlik balked at the ground-up codebase rewrite, which might have been prompted by the advent of Tableau in the mid-2000s. It's now left with one package that has a semi-'legacy' status, and another more modern offering that perhaps omits too many of the powerful features of the original.
The long-term prospects of both frameworks are an additional consideration. The nature of the $15.3 billion purchase of Tableau by Salesforce suggests an evolutionary rather than predatory change of ownership. Yet, the ramifications of any potential corporate-led changes of direction or policy are still an unknown factor.
Against these challenges and the headwind currently enjoyed by Microsoft in the BI software space, there is also a raft of insurgent contenders looking to unsettle the old guard, including Domo, ThoughtSpot, ClearStory, and Looker, among others. If 'recent provenance' was the sole compelling factor, one would have to overlook both QlikView and Tableau!
But experience counts: QlikView and Tableau are known, productive quantities with a mature base of developers to draw from, and, respectively, impressive client lists; and each has fought for and won a precious vanguard space in a very crowded market.
If you need pre-optimized, highly responsive presentations and can commit to the extra scripting and diminished hand-holding that QlikView requires to achieve it (and the hardware upgrades that often become necessary when QlikView implementations start to scale), then this seems an apposite choice.
If your needs are more exploratory and less architectural, Tableau offers a flexible and powerful data discovery experience that can reveal new inter-relationships between data sources with the facility and flexibility of a framework that was primarily designed for this purpose.
Find out about the ecommerce business intelligence tools to keep an eye on in 2020, and learn more about their features.
Looking for an open-source machine learning library? This guide explains the pitfalls and advantages of two of the most popular libraries from Google’s and Facebook's AI portfolios.
Angular and React have plenty in common. However, it is the differences between the two products that define their suitability. In this article, we highlight some of those to help you decide whether to create your next web app with the Angular framework or the React library.
If you want to know the difference between mobile device management and enterprise mobility management, and which one to choose, this EMM vs. MDM guide is just what you need.
WANT TO START A PROJECT?