Likelihood to Recommend Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
Read full review Tableau Desktop is one the finest tool available in the market with such a wide range of capabilities in its suite that makes it easy to generate insights. Further, if optimally designed, then its reports are fairly simple to understand, yet capable enough to make changes at the required levels. One can create a variety of visualizations as required by the business or the clients. The data pipelines in the backend are very robust. The tableau desktop also provides options to develop the reports in developer mode, which is one of the finest features to embed and execute even the most complex possible logic. It's easier to operate, simple to navigate, and fluent to understand by the users.
Read full review Pros Its performance, ease of use, and simplicity in learning and deployment. Using this tool, we can quickly analyze large amounts of data. It's adequate for map-reducing large datasets and fully abstracted MapReduce. Read full review 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. Read full review Cons UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors. Being in early stage, it still has a small community for help in related matters. It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement. Read full review Formatting the data to work correctly in graphical presentations can be time consuming Daily data extracts can run slowly depending on how much data is required and the source of the data The desktop version is required for advanced functionality, editing on [the] Tableau server allows only limited features Read full review Likelihood to Renew 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.
Read full review Usability It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Read full review 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.
Read full review Reliability and Availability 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.
Read full review Performance 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
Read full review Support Rating The documentation is adequate. I'm not sure how large of an external community there is for support.
Read full review I have never really used support much, to be honest. I think the support is not as user-friendly to search and use it. I did have an encounter with them once and it required a bit of going back and forth for licensing before reaching a resolution. They did solve my issue though
Read full review In-Person Training 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.
Read full review Online Training The training for new users are quite good because it covers topic wise training and the best part was that it also had video tutorials which are very helpful
Read full review Implementation Rating 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.
David Fickes Decision Sciences - Modeling, Simulation & Analysis
Read full review Alternatives Considered Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Read full review If we do not have legacy tools which have already been set up, I would switch the visualization method to open source software via
PyCharm ,
Atom , and
Visual Studio IDE . These IDEs cannot directly help you to visualize the data but you can use many python packages to do so through these IDEs.
Read full review Scalability 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.
Read full review Return on Investment Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with. Read full review 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. Read full review ScreenShots