Magnolia vs. Tableau Desktop

Magnolia is a very capable DXP, that provides client with lots of flexibility in composing its own stack. While the core of the platform is a content management system, the open architecture of Magnolia DXP allows it to connect to any platform, allowing client to extend the capabilities. One scenario would be a centralized content hub - where through a single platform, content authors can choose which channel to distribute what content. For example, long form content for consumers viewing on a laptop, short form content for those using a mobile browser. This allow the client to personalized the experience based on channels. Another scenarios would be leveraging on GenAI - using Magnolia's built-in connector to ChatGPT. If that is not the service that one desire, you can always connect to another AI service such as Google Gemini. With GenAI, connected, content author can use AI as co-pilot to help them scale up their content production.

The best scenario is definitely to collect data from several sources and create dedicated dashboards for specific recipients. However, I miss the possibility of explaining these reports in more detail. Sometimes, we order a report, and after half a year, we don't remember the meaning of some data (I know it's our fault as an organization, but the tool could force better practices).

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• Speed of development - time to delivery from zero to MVP was excellent
• Ease of use - the authoring experience is very easy to build and train
• PAAS/SAAS - the managed service platform removed the traditional overhead of running in-house technologies, meaning we could focus on value add, with less time spent keeping the lights on.

• An excellent tool for data visualization, it presents information in an appealing visual format—an exceptional platform for storing and analyzing data in any size organization.
• Through interactive parameters, it enables real-time interaction with the user and is easy to learn and get support from the community.

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• The documentation provides samples that are often out of context, and difficult to know where the provided example code should be implemented. More tutorials providing the full project or step-by-step instructions on how to implement subject material would help greatly. Baeldung is a resource I would consider the gold standard in how this is done in other spaces.
• The use of JCR and Nodes makes object serialization/deserialization painful. Jackson compatibility or similar would be a welcome enhancement to the developer experience. Maybe leveraging code-gen from light modules to build model classes when possible could help accomplish this.
• Modifying the home layout from light modules is frustrating. It seems that any configuration overrides made merge with the default rather than overwriting, which makes for a difficult combination of guess-and-check while referencing the documentation to see what should be in each row/column when making changes.
• Including "mark all as read" or "delete all" in the notifications app would be a great quality of life improvement. It seems that by default, users have to individually select messages and operate them.

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• Pricing should be more user-friendly and usage-driven
• Making edits to the production reports is fairly tough and has a vast scope of additional capabilities
• Tableau Desktop should be able to differentiate itself from the Tableau server else there is no major meaning of two different products being offered

We have invested a lot of time and energy into tailoring a solution that works for the company.

We think the new features in v6.2 will help us get to the next level

We also don't have the resources to rebuild a website platform from scratch even if we wanted to

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Our use of Tableau Desktop is still fairly low, and will continue over time. The only real concern is around cost of the licenses, and I have mentioned this to Tableau and fully expect the development of more sensible models for our industry. This will remove any impediment to expansion of our use.

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We've shown it to a number of users both clients and our own team and despite initial apprehensions, they "get it" very quickly. It's intuitive and friendly and quick to perform daily tasks. We once had a client tell us "Using Magnolia makes me smile" which says it all for us.

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Tableau Desktop has proven to be a lifesaver in many situations. Once we've completed the initial setup, it's simple to use. It has all of the features we need to quickly and efficiently synthesize our data. Tableau Desktop has advanced capabilities to improve our company's data structure and enable self-service for our employees.

When used as a stand-alone tool, Tableau Desktop has unlimited uptime, which is always nice. When used in conjunction with Tableau Server, this tool has as much uptime as your server admins are willing to give it. All in all, I've never had an issue with Tableau's availability.

I gave [it] 7/10 only because of the loading time of pages. Otherwise, I think it deserves an 8. Normally this is not an issue per [se] but considering the rating matrix and as I have been asked to honestly write about it. Yes, the page loading times could be improved.

Tableau Desktop's performance is solid. You can really dig into a large dataset in the form of a spreadsheet, and it exhibits similarly good performance when accessing a moderately sized Oracle database. I noticed that with Tableau Desktop 9.3, the performance using a spreadsheet started to slow around 75K rows by about 60 columns. This was easily remedied by creating an extract and pushing it to Tableau Server, where performance went to lightning fast

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You always get an answer based on your SLA. But you always get a solution. That's the successfactor in this case. To often i was frustrated about people in a company without even a clue what there product is about or how to solve a problem. Magnolia's Support Team does a very good job and try to help you in most of the cases

Tableau support has been extremely responsive and willing to help with all of our requests. They have assisted with creating advanced analysis and many different types of custom icons, data formatting, formulas, and actions embedded into graphs. Tableau offers a weekly presentation of features and assists with internal company projects.

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It is admittedly hard to train a group of people with disparate levels of ability coming in, but the software is so easy to use that this is not a huge problem; anyone who can follow simple instructions can catch up pretty quickly.

I think the training was good overall, but it was maybe stating the obvious things that a tech savvy young engineer would be able to pick up themselves too. However, the example work books were good and Tableau web community has helped me with many problems

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Again, training is the key and the company provides a lot of example videos that will help users discover use cases that will greatly assist their creation of original visualizations. As with any new software tool, productivity will decline for a period. In the case of Tableau, the decline period is short and the later gains are well worth it.

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I've used several CMSs like AEM and EpiServer, and comparatively, they all excel at different things. Magnolia is the best to develop for/against. Episerver has the best/most fluid UI in terms of content editing, and the overall admin experience AEM is just all around sucks.

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I have used Power BI as well, the pricing is better, and also training costs or certifications are not that high. Since there is python integration in Power BI where I can use data cleaning and visualizing libraries and also some machine learning models. I can import my python scripts and create a visualization on processed data.

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Tableau Desktop's scaleability is really limited to the scale of your back-end data systems. If you want to pull down an extract and work quickly in-memory, in my application it scaled to a few tens of millions of rows using the in-memory engine. But it's really only limited by your back-end data store if you have or are willing to invest in an optimized SQL store or purpose-built query engine like Veritca or Netezza or something similar.

• Magnolia has brought about positive impacts. For instance, we need not outsource web design and marketing services because thanks to this software, we can handle most work inhouse
• The software is affordable with no compromises on capabilities and therefore it is gives us value for money.
• The templates makes the whole process easy

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• Tableau was acquired years ago, and has provided good value with the content created.
• Ongoing maintenance costs for the platform, both to maintain desktop and server licensing has made the continuing value questionable when compared to other offerings in the marketplace.
• Users have largely been satisfied with the content, but not with the overall performance. This is due to a combination of factors including the performance of the Tableau engines as well as development deficiencies.

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