Jupyter Notebook vs. MicroStrategy Analytics vs. TensorFlow

I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.

Incentivized

MSTR is great for any organization that is looking for a way to deliver complicated data in an uncomplicated way. From business teams to marketing and finance, several departments benefit from using MSTR to keep track of KPIs enabling teams to make optimizations along the way. MSTR provides great visual representations of data enabling team members to distill thousands of data points into easily digestible charts and graphs

Incentivized

TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).

Incentivized

• Simple and elegant code writing ability. Easier to understand the code that way.
• The ability to see the output after each step.
• The ability to use ton of library functions in Python.
• Easy-user friendly interface.

Incentivized

• They sell the product well, and make promises you will actually believe
• "checks the box" for most features a company would need. Doesn't actually deliver them though
• They answer the phone in a timely manner. Can't answer your questions or provide support, but the queue time isn't bad
• They have online documentation. It's not up to date, and likely doesn't reflect the version of software you are using, but hey... they can point to it.

• A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
• Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
• Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.

Incentivized

• Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
• Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.

Incentivized

• The graph options are not as robust as an Excel user might want.
• The metadata layer can sometimes be slower, and it takes a long time for reports to run.
• Viewing the data feed entered can be cumbersome at times, in terms of validation with reports.

Incentivized

• RNNs are still a bit lacking, compared to Theano.
• Cannot handle sequence inputs
• Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.

I would always choose to renew MicroStrategy as long as they lead the market in features, functionality and price. The support of MicroStrategy is timely and professional, I frequently get answers to my questions within 24 hours and normally have solutions within 48 hours. Training available for MicroStrategy completely covers everything required to be able to expertly use MicroStrategy and understand data warehousing.

Incentivized

Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.

Incentivized

The standard grid reporting could look more like the styling and object used for the Import and Visual Insight products. In addition, object properties almost seem to be hidden when first using the product. It's as if they are asking the engineers to only use the presets we make available...and, these presets are 10+ years old. On the positive side, Microstrategy seems to be the only product, not named Cognos, which can scale to Big Data. The product is "hackable" via the SDK or tricking the Intelligence Server to do uncommon things. The Microstrategy development team also seems to be very involved with their OEM partners; especially when it comes to features and enhancements. A large majority of the improvements we suggested have made it into the product or on the roadmap for future enhancements. Only suckas fall for the shiny objects from most other vendors; Microstrategy is really the only choice for Enterprise BI.

Support of multiple components and ease of development.

Incentivized

I've never had an issue with MicroStrategy not being available due to MicroStrategy application malfunction. It is very robust and only failures I've seen were due to user error or the platform the machine running the service failed some how.

Incentivized

Being able to customize the performance based on the business need is extremely powerful. Proper configuration and understanding of the usage pattern is key, if the technical ability of the architect is not at top level, then the product will not be configured correctly which will lead to poor performance.

Incentivized

I haven't had a need to contact support. However, all required help is out there in public forums.

Incentivized

Good user community. Support team is available if you are under AMC. You get decent support after raising the support ticket. If it is product bug they will inform you and let you know which patch will resolve the same.

Incentivized

Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.

Incentivized

I have attended many trainings offered by MicroStrategy; both distance and in-person training. I earned my CRD (Certified Report Developer) certification via the online training. I found the training to be well organized and concise. Overall I will definitely continue to increase my knowledge with MicroStrategy via the online training offering.

We measured following KPI for the success

• Increase in Business Feeds Loads
• Reduction in Controllers Manual Work.
• Conversion of Manual Work into Systematic Workflows.
• Increase in Customer Spend
• Reduction in Service costs
• Reduction in time-to-market for new campaigns
• Increase in cross sell and Up sell
• Error reduction in customer Identification
• Existing client retained

Incentivized

Use of cloud for better execution power is recommended.

Incentivized

With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.

Incentivized

Tableau is probably MicroStrategy Analytic's biggest competitor I've noticed over time, and I'm not sure why. Tableau only covers visualizations independently for each business user, which then creates the issues of every employee creating their own version of the data, and then you have 20 versions of the truth. A enterprise data warehouse and MicroStrategy's Visual Insight is a better method.

Incentivized

Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice

Incentivized

This software is extremely scaleable, one can add more core servers which performs as a load balancing. The configurations available to manage usage patterns and daily activity are as high a caliber as any other enterprise level software. This product can be installed on both a windows and unix platform allow for integration on a budget.

Incentivized

• Positive impact: flexible implementation on any OS, for many common software languages
• Positive impact: straightforward duplication for adaptation of workflows for other projects
• Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration

Incentivized

• MicroStrategy was helpful for reducing the amount of time we needed to spend number crunching large data sets, and in doing so, allowed me as the primary users to spend more time gleaning insights from the data that in turn informed our leadership team to make strategic decisions.
• By creating numerous canned reports available to all members of the team through email distribution or basic access to the platform, we were able to reduce the time I spent showing people how to pull the data in Microsoft Excel by nearly 40% .
• We ended up needing to make many changes to the way our DMP was feeding data into MicroStrategy due to incorrect reporting that caused complications in accounting and finance.

• Learning is s bit difficult takes lot of time.
• Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
• Once you have learned this, it make your job very easy of getting the good result.

Incentivized