From this point of view Tableau is a much better choice for serious analytical applications. Tl;dr; For content that doesnt require drm, were there. If you need drm theres still some work ahead. At wwdc 2016 Apple had some big news for the video streaming community. They announced two big changes that move us closer to a world where media files can truly be shared. Announcement one was that the hls (http live streaming) specification would be expanded to allow for use of fMP4 (fragmented MP4) media segments. The second announcement was that fairPlay would support fMP4 segments that are encrypted with cenc.
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Qlikview please review the worksheet below: Qlikview portfolio includes Client tool, server, publisher, Information Access and Extranet Servers (as well as others like workbench, pdf reporting Server and at least three non-standard Data connectors (in addition to odbc and standard data connectivity) pca uses Qlikview. Pca uses Qlikview Server to convert data visualizations to multi-user Web applications, to manage users and groups, to automate data updates and to provide security for users and data. With Qlikview Publisher the pca centralizes the distribution of Qlikview applications and automates data updates. Qlikview Information Access Server allows unlimited public access to one visualization while Qlikview Extranet Server allows access to three visualizations via private web site(s). Even on portfolio level, Qlikview has some warning flags/points from pca point of view: portfolio is more complicated compare with Tableau, data connectivity is less sophisticated, multi-tenancy is not supported, and access to different Data visualizations (Documents in Qlikviews speak) is limited either by server. And yes, the access to each Qlikview Document (Data visualization) requires 350 fee per named user per document. On other hand, tableau is the only Platform which allows to pca to distribute data visualizations and Interactive visual Reports server-less and for free, while maintaining the central Database, which is the (Central db that is) advantage of all Smart Client applications, developed by pca. Pca uses Qlikview scripting for deeper customization of Data visualizations if needed. Pca experience is that the Analytical applications in many cases need to be able to use olap cubes from ssas (sql server Analytic Services) and Excel Powerpivot as data sources while preserving all Cube infrastructure, design, dimensions, measures and calculations. Qlikview currently does not support this functionality while tableau is only advanced fat Data visualization tool which can use ssas cubes and connect to powerpivot.
While Qlikview in-memory columnar database has a more advanced and faster data engine, tableau is able to use the disk space as a virtual memory to enable unlimited/scalable data for visualizations. In the data Engine department, Qlikview is preferable if the analyst needs speed; Tableau is usually the better choice if scalability is the main requirement, and Tableau is generally used for pca projects. Tableau has a simple portfolio: Desktop, server and reader: pca uses Tableau desktop to design data visualizations as Windows applications, to make the data visible, to enable analysts to interact with data and analyze them on their desktop and to deploy visualizations to tableau server. Pca uses Tableau server to convert data visualizations to multi-user web applications, to manage users and groups, to automate data updates and to provide multi-tenancy and security for users and data. Tableau reader is a free windows application and pca uses it to enable server-less distribution of data visualizations, to reduce it requirements and to make world-class visual analytics available even to small businesses. Tableau provides all Data connectors, Adapters and Drivers (about 30 of them, in addition to odbc, flat files, odata, azure) to multiple data sources for free. Tableau has an excellent and native mapping pdf functionality. For a more detailed comparison of Tableau.
As vendor, tableau is clearly on rise and umum the company seems healthy. Their product is developing rapidly, which unfortunately is not the case resume with Qlikview - i'm getting impression that Qliktech chronically under-invest in r d and put too much on sales and marketing. There are lots of things in Qlikview which wait to be modernized and improved very long time. However, its main killer feature - ultra-fast in-memory engine - is still unbeatable. Posted by Dmitry gudkov tableau vs Qlikview Practical Computer Applications (PCA) evaluates Tableau, qlikview and other excellent data visualization and bi products to select the most appropriate software to solve our clients business problems. Pca implements Tableau and Qlikview Visualizations so most charts behave as interactive synchronized data filters and support advanced visual drill down, slicing and dicing functionality. Pca uses sophisticated calculated fields and formulas in both tools to implement custom business logic. Pca uses the in-memory data engine, provided by both Qlikview and Tableau to enable fast interactions with huge data sets.
However since loading script in Qlikview is capable to perform light etl and data cleansing therefore in many cases this is sufficient enough. Lack of collaboration activities - while i'm not quite excited about the way data annotation is done in Qlikview (here is my point of view on data annotation) however it's present at least in this form and instant application sharing is simply awesome feature Tableau. Resume tableau is an excellent q a tool which is very well designed and suited for non-technical users. It is powerful, easy to use, highly visual and aesthetically pleasant. Good evidence that Tableau is a good fit for business users was audience of tcc2012 - there were a lot of women and at the same time there were not many Indian developers which is not a typical case for a bi event. However, promise of Tableau's execs and sales that "it is not required with Tableau" is much less true than it might seem because involvement of it personnel could be higher than expected as more complex dashboards become required. Qlikview applications usually require it developers to create them, however the developers get much more flexible and powerful toolkit that allows them to create very information rich dashboards with fixed layout.
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Contrary to Qlikview, tableau makes bet not on syntax and scripting but on various assignment settings and actions performed via user interface. While it is good for fast start and early learning curve, as page complexity of dashboards increases it eventually leads to necessity of knowing various hacks, tricks and workarounds. For instance making objects (charts, tables, text labels) appear or disappear depending on some parameter (variable) is a straight forward task in Qlikview but is actually a hack in Tableau. And that's not good, because in case of Qlikview there is albeit complex but logical and well documented syntax but in case of Tableau you will need to learn these hacks and tricks from someone else. Because sometimes it's near to impossible to understand logic behind them without help of more experienced developer. So finally experience of a user becomes largely defined by amount of various collected hints and trick. Use of screen estate.
It's hard to compete with Qlikview in efficiency of screen estate use. Qlikview offers various gadgets, in-line minicharts, easy management of object visibility which allows making dashboards very information rich. While it's a usual thing for a qlikview dashboard to have 10 listboxes, having 10 quick filters (analogue of listbox) on Tableau dashboard will most probably make it completely cluttered and barely usable. Also such thing as in-line minicharts simply doesn't exist in Tableau. Need for an etl. As any other bi tool that heavily relies on database engine tableau needs cleansed and transformed data. In general this also is true for Qlikview.
Tableau enhances source data with its own data (e.g. Calculated fields, dynamic groups, sets, latitude and longitude for locations) and also makes in-memory cross-source joins. Therefore query optimization requires good understanding of how Tableau performs these operations under the hood, how it interacts with database and what are implications of different settings. This makes task of performance optimization even less trivial. I've got impression that performance is not something that Tableau is ready to boast about.
Calculation in-memory engine is much weaker and far not so sophisticated as Qlikview's one. Not significant but interesting detail - two demo databases in standard Tableau desktop installation have only 4'248 and 8'399 records respectively. One more reason for concern is that both Tableau desktop and Tableau server exist only in 32-bit version. We were told that 64-bit version is being actively developed however as of now the only version available for customers is 32-bit. Having in mind these concerns about performance i'm not sure that ability to directly work with very large datasets really becomes an advantage on practice. Yes, theoretically it's better to be able to query 10tb of data than not. But would it have any practical use if this could require a few hours of waiting time?
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Working with Qlikview it's easy to forget what performance optimization. Surely, there are some tricks how to improve performance for very large datasets (hundreds of millions of records) but this question rarely appears in daily agenda of a buy qlikview developer. Having subsecond response time on 20 millions of records even on a laptop is not something unusual. However, i suspect that question of performance optimization will global rise much more often for Tableau applications which heavily rely on relational databases. And that's not so trivial task as it may seem. Sql query optimization is not a trivial task itself and may include special indexing strategy, joins optimization strategy and use of various hints and tricks - task that requires experienced database professionals and is simply impossible for a business user (who has a lot. However that wouldn't be so dramatic if that was the only thing.
However, creating naming sets on the fly, applying set algebra operations (introduced in Tableau 8) to them like addition, intersection, subtraction and calculating aggregates against sets is a very useful and practical feature, for some reason underestimated and neglected by major bi vendors. I can recall only businessObjects Set Analysis which was quite clumsy last time when I saw it a few years ago. Qlikview has not much to offer here. While it is possible to save different selections into bookmarks it's not possible to apply set algebra to them. Comparison of aggregates of two ad hoc sets is possible but requires shrek's knowing rather complex set analysis expression syntax which is a non-trivial task for even advanced business users. How many of them are capable to quickly write something like this? Sum(Set1 year:Year, month:Month Amount) - sum(Set2 year:Year, month:Month Amount) Despite developers of a qlikview application can implement comparison of sets in a dashboard, it's not available out of the box. Dynamic grouping (of dimensions) is not possible in Qlikview at all - grouping requires creating additional data structures and reloading the application. Now let's talk about some disadvantages of Tableau.
features in Tableau. Usually maps is a real pain for bi developers because support for maps usually is rather poor in bi platforms. Some of them provide mapping functionality via integration with 3rd party gis platforms like esri or MapInfo. But level of integration is never good enough, not to forget additional licensing costs. Qlikview) imitate mapping by offering maps simply as a colorful background for scatter charts, without important capability of highlighting regions or providing additional visual layers. Tableau has done a good work here and offers excellent mapping functionality which includes regularly updated maps and complimentary information (e.g. Population or income) licensed from 3rd parties (without any additional costs for customers). Ability to dynamically group dimensions is not something unseen before in bi suites (e.g.
By the way, in French tableau has two meanings - painting and table. Excellent match of brand and product concept. Drag-n-drop authoring as cornerstone of analysis ilahi and design processes. That's what WebIntelligence was good at, but Tableau makes it even better, simpler and easier. Sadly Qlikview has almost nothing to offer here - fields still have to be picked from a cluttered properties dialogs and dashboards have rather static layout. In Tableau there are (at least) two special types of dimensions : time and location. I like the idea of special dimensions in general because indeed some dimensions should be treated differently for more efficient analysis and Tableau demonstrates this very well.
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Open thread 157, posted by Abi sutherland at 02:09. —terry Pratchett, lords and Ladies, extensions? Back to, open thread 156. First thing I'd like to tell - starting from version 8 Tableau can honestly be considered a truly mature product - a big difference with what I saw two years ago. It's a smartly designed, feature-full and powerful analytic tool which is especially good for ad hoc query and analysis buy (Q A). Prior to tableau i considered BusinessObjects WebIntelligence to be the best q a tool on the market. However, in my picture of bi world this honorable title now belongs to tableau. Here is what I liked (not in order of importance state-of-art data visualization makes Tableau outstanding in the crowd of bi suites. Tableau people talk about "being creative with data" and it's easy to believe in this while looking at clean and elegant Tableau dashboards.