Likelihood to Recommend Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.
Read full review A high level of data integration is available here it supports various data sources and so on. Collaborating features allow users to give access to the dashboard and merge data analytics with other team members. It can meet the demands of both small and large size business enterprises. A customized dashboard and reports are provided to meet the specific needs and get support of extensibility through APIs and customized scripts.
Read full review Pros Excellent analytical and prediction tool In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model Readily available algorithms, easy to use in your analytical projects Faster than Python scikit learn (in machine learning supervised learning area) It can be accessed (run) from Python, not only JAVA etc. Well documented and suitable for fast training or self studying In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow It can be used as open source Read full review It has the best coding integration (python, R) of any BI product The ability to work with very large datasets (10 mil+) is better than competitors Export options are more complete and have better functionality The data canvas is the best tool to join and transform data vs. competitors Jim Putnam Director, Advanced Analytics and Data Science
Read full review Cons Better documentation Improve the Visual presentations including charting etc Read full review The donut chart is I guess a powerful illustrations but I hope it should be done quite simple in Spotfire. But in Spotfire there are lots of steps involve just to build a simple donut chart. Table calculation (like Row or Column Differences) should be made simple or there should be drag and drop function for Table Calculation. No need for scripting. Information Link should be changed. If new columns are added to the table just refreshing the data should be able to capture the new column. No need extra step to add column Read full review Likelihood to Renew -Easy to distribute information throughout the enterprise using the webplayer. -Ad hoc analysis is possible throughout the enterprise using business author in the webplayer or the thick client. -Low level of support needed by IT team. Access interfaces with LDAP and numerous other authentication methods. -Possible to continually extend the platform with JavaScript, R scripts, HTML, and custom extensions. -Ability to standardize data logic through pre-built queries in the Information Designer. Everyone in the enterprise is using the same logic -Tagging and bookmarking data allows for quick sharing of insights. -Integration with numerous data sources... flat files, data bases, big data, images, etc. -Much improved mapping capability. Also includes the ability to apply data points over any image.
Read full review Usability Basic tasks like generating meaningful information from large sets of raw data are very easy. The next step of linking to multiple live data sources and linking those tables and performing on the fly analysis of the imported data is understandably more difficult.
Read full review Reliability and Availability Even though, it's a rather stable and predictable tool that's also fast, it does have some bugs and inconsistencies that shut down the system. Depending on the details, it could happen as often as 2-3 times a week, especially during the development period.
Alex Naumov Global Pricing and Marketing Operations Lead, Analytics & Research
Read full review Performance Generally, the Spotfire client runs with very good performance. There are factors that could affect performance, but normally has to do with loading large analysis files from the library if the database is located some distance away and your global network is not optimal. Once you have your data table(s) loaded in the client application, usually the application is quite good performance-wise.
Read full review Support Rating The overall experience I have with H2O is really awesome, even with its cost effectiveness.
Read full review Support has been helpful with issues. Support seems to know their product and its capabilities. It would also seem that they have a good sense of the context of the problem; where we are going with this issue and what we want the end outcome to be.
Tim Daciuk Product Manager - Mobile Computing Analytics Cloud Platform
Read full review In-Person Training The instructor was very in depth and provided relevant training to business users on how to create visualizations. They showed us how to alter settings and filter views, and provided resources for future questions. However, the instructor failed to cover data sources, connecting to data, etc. While it was helpful to see how users can use the data to create reports, they failed to properly instruct us on how to get the dataset in to begin with. We are still trying to figure out connections to certain databases (we have multiple different types).
Read full review Online Training The online training is good, provides a good base of knowledge. The video demonstrations were well-done and easy to follow along. Provided exercises are good as well, but I think there could be more challenging exercises. The training has also gone up in price significantly in the last 3 years (in USD, which hurts us even more in Canada), and I'm not sure it is worth the money it now costs (it is worth how much it cost 3 years ago, but not double that.)
Read full review Implementation Rating The original architecture I created for our implementation had only a particular set of internal business units in mind. Over the years, Spotfire gained in popularity in our company and was being utilized across many more business units. Soon, its usage went beyond what the original architectural implementation could provide. We've since learned about how the product is used by the different teams and are currently in the middle of rolling out a new architecture. I suggest:
Have clearly defined service level agreements with all the teams that will use Spotfire. Your business intelligence group might only need availability during normal working hours, but your production support group might need 24/7 availability. If these groups share one Spotfire server, maintenance of that server might be a problem. Know the different types of data you will be working with. One group might be working with "public" data while another group might work with sensitive data. Design your Library accordingly and with the proper permissions. Know the roles of the users of Spotfire. Will there only be a small set of report writers or does everyone have write access to the Library? ALWAYS add a timestamp prompt to your reports. You don't want multiple users opening a report that will try and pull down millions of rows of data to their local workstations. Another option, of course, is to just hard code a time range in the backing database view (i.e. where activity_date >= sysdate - 90, etc.), but I'd rather educate/train the user base if possible. This probably goes without saying, but if possible, point to a separate reporting database or a logical standby database. You don't want the company pounding on your primaries and take down your order system. Read full review Alternatives Considered Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around
TensorFlow seems larger than that of H2O.
Read full review Spotfire is significantly ahead of both products from an ETL and data ingestion capability. Spotfire also has substantially better visualizations than Power BI, and although the native visualizations aren't as flexible in
Tableau , Spotfire enables users to create completely custom javascript visaualizations, which neither
Tableau or Power BI has.
Tableau and Power BI are likely only superior to Spotfire with respect to embedded analysis on a website.
Read full review Scalability In an enterprise architecture, if Spotfire Advanced Data services(Composite Studio),data marts can be managed optimally and scalability in a data perspective is great. As the web player/consumer is directly proportional to RAM, if the enterprise can handle RAM requirement accomodating fail over mechanisms appropraitely, it is definitely scalable,
Read full review Return on Investment Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product Read full review It is costly, so not suitable for small scale implementations. Dashboards are as good as the developer, so need experience to get most out of it You need to be on Spotfire 11 at least to implement out of the box visualizations Integration with Python and R is a game changer, it comes very handy to onboard data scientists without much hassle performance is exceptionally well. Secure Read full review ScreenShots