Powerful, Efficient and Customizable Data Analytcs
Updated October 22, 2022
Powerful, Efficient and Customizable Data Analytcs
Score 9 out of 10
Vetted Review
Verified User
Software Version
TIBCO Cloud Spotfire Enterprise
Overall Satisfaction with TIBCO Spotfire
Spotfire is an amazing choice as a BI Analytics platform. It provides end to end solution of all Data Analytics needs, with all cutting age modern technology integrations available. It is a solution as a whole or as a small piece of implementation. It offers compete control over the components and a rich API to automate it. Developers feel empowered and are free to choose technology of their choice like TERR, Python, IronPython, JavaScript etc.; and without much learning curve they can start contributing with the help of their existing skill-set. Spotfire is a scalable, trust worthy, available and secure platform as a Summary.
- Innovation
- Availability of trending technology
- Integrations with Data Science Platforms
- Easy to use
- Complete control over different components
- They should introduce Python native visualizations
- Learning Materials for basic and Intermediate level should be made free. This helps to attract and onboard new users.
- Jupyter integration will be a great improvement.
- There should be approach available for parallel development. For Example, Two person should be able to edit and save same dashboard if they are working with different components.
- 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
- Online Training
- In-Person Training
I was able to explore some untouched areas this platform like Expression Shortcuts, Mods Development, Data Functions tips and tricks etc.
Also, Spotfire training helped to save a lot development time by demonstrating the shortcuts present in system. I was able to understand some nice features like in-built predictive algorithms, classification and clustering approaches etc.
Also, Spotfire training helped to save a lot development time by demonstrating the shortcuts present in system. I was able to understand some nice features like in-built predictive algorithms, classification and clustering approaches etc.
I have been using Spotfire with some of other Spotfire and TIBCO products too, like Spotfire Data Science and TIBCO Data virtualization. These products are well supported by Spotfire and they come with native integration. For example, TDV needs no specific setup before starting with Spotfire as there is a native connector. Also, as the both products have same vendor - we do not face challenges related to product support.
Spotfire's key strength les in extent of customization possible and it's inherent Data Analytics capabilities. With in-memory and in-database analysis capabilities, it comes out as a high performance and high efficiency BI solution.
Adding to it, Spotfire integrates the Python and TERR engines very effectively to implement out of the box solutions seamlessly. Unlike Power BI and Tableau ; you don't need any separate Python/R instance configured to play with these Data Science technologies.
it is more like a Visual Data Science Platform.
Adding to it, Spotfire integrates the Python and TERR engines very effectively to implement out of the box solutions seamlessly. Unlike Power BI and Tableau ; you don't need any separate Python/R instance configured to play with these Data Science technologies.
it is more like a Visual Data Science Platform.
Do you think Spotfire delivers good value for the price?
Yes
Are you happy with Spotfire's feature set?
Yes
Did Spotfire live up to sales and marketing promises?
Yes
Did implementation of Spotfire go as expected?
Yes
Would you buy Spotfire again?
Yes
Spotfire Feature Ratings
TIBCO Cloud Capabilities
TIBCO Spotfire Cloud Analyst approach is definitely something great for small and mid sized organizations. To start with any kind of on-premises setup, the organization spend a lot of time and money in achieving economies of scale. They make actual benefits only when they scale at large. However, with the introduction of Cloud capabilities, they can start their data analytics journey almost immediately. Adding to it, they need not to worry about Administration cost and related overhead.
Honestly, as an end user - there will not be much difference. For developers, only a slight changes were there to deal with. We migrated from on-premises structure to hybrid cloud and experience was not much different for usage and performance.
However, the real impact was on availability part. The platform availability was a challenging task in old setup as taking care of usage, optimization of load and uptime is costlier to run. Cloud is very flexible and always available to serve.
However, the real impact was on availability part. The platform availability was a challenging task in old setup as taking care of usage, optimization of load and uptime is costlier to run. Cloud is very flexible and always available to serve.
Yes, we were able to do so. Challenging part was adaptation of newer cost model.
In on-premises approach, we needed to incur a heavy investment in initial phase of establishment only and needed to spend time to achieve economies . Also, the operations was difficult because regardless of analysis is being used or not, we are investing resources into it.
With cloud structure, the charges do increase with usage. So idle resources do contribute to a very little impact. Though there are challenges for some services like Scheduled update are still in force, but overall we are in win-win situation.
In on-premises approach, we needed to incur a heavy investment in initial phase of establishment only and needed to spend time to achieve economies . Also, the operations was difficult because regardless of analysis is being used or not, we are investing resources into it.
With cloud structure, the charges do increase with usage. So idle resources do contribute to a very little impact. Though there are challenges for some services like Scheduled update are still in force, but overall we are in win-win situation.
I would say, they were mostly there; not completely. When we adapt to the new structure, the costs do shoot up with usage at scale. Whatever we were saving on hardware, we ended up investing in services.
Functionality wise, Cloud delivered access and availability; but we had to sacrifice some features too. For example, in Cloud Analyst - we cannot run most of the scripts.
Functionality wise, Cloud delivered access and availability; but we had to sacrifice some features too. For example, in Cloud Analyst - we cannot run most of the scripts.
- Low cost to start
- High Availability
- Ease to develop analytics solution
Spotfire is great at customer support part. The only recommendation is, use LTS rather than MS versions for better ongoing support. This way you will have a continued quality of service from them. They are reachable and prompt, however the difference of time zone need to he addressed. There is a lot of wait time involved un-necessary.
Though there were lot of recommendations were taken up, but not the all required. For example, version for IronPython is still set to 2.7 which is based on Python 2 paradigm. This involves a learning curve for script developers. Need was to adopt a unified standard for all tech in bracket e.g. Python Data Function and IronPython in similar syntactical structure at least.
There are lot of requests on customization side are still long awaited.
There are lot of requests on customization side are still long awaited.
- Data Democracy - Freedom to select the view of data with no code
- Python Data Function for Out of the Box implementations
- Chatbots in Spotfire
Evaluating TIBCO Spotfire and Competitors
Yes - Tableau was replaced by Spotfire, as it needed a complex Data to Dashboard architecture. The Data needed to be prepared separately for reporting and that process took a lot of time to reach our final goals. The time to production was slower, as we needed to wait for data to be prepared first. Also, using different products at each step was painful.
- Product Features
- Product Usability
- Prior Experience with the Product
Performance, Customization and Control over the tool were the key players in the decision. Spotfire was flexible to cater the codes developed earlier with Python and R script; without loosing their utility. Also, it was easier to transition capabilities between systems. For example, We had a Python code for data extraction and transformation and Visualization was getting out of hands. At this moment, we were able to transfer the completed chunks to Spotfire and let the tool handle visualization part of it.
The process was very satisfactory though, still if we have to reconsider this - it will be better to evaluate the prices across different offerings. For example, Spotfire in AWS was a good choice from price and implementation point at start, but as the size and scale went up; we were able to realize that AWS was growing costlier very soon. On-Premise implementation is costlier at start, but becomes cost effective at large scale.
In short, while considering cost; consider cost over the time rather than cost at the time.
In short, while considering cost; consider cost over the time rather than cost at the time.