Jupyter Notebook vs. Microsoft BI (MSBI)

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

Microsoft BI is well suited for Stream analytics, easy data integration, report creation and UI/UX designs (limited but what all available are great ones) Microsoft BI may be less appropriate for handling huge number of datasets and difficult queries. It may also be difficult for a company with heavy data.

• 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

• Comparatively easy to use compared to other data analytics solutions, collaborating with other colleagues on data work is simple.
• Using Visual Studio for database, ETL, reporting, and analytics development save time and money.
• Transfer of data from one application to another via Excel and comparison of data attributes between applications
• Dashboard functionality, as well as Python support, are available, allowing you to add additional charts and graphs.

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 race to perfect gathering of Non-Traditional datasets is on-going; with Microsoft arguably not the leader of the pack in this category.
• Licensing options for PowerBI visualizations may be a factor. I.e. if you need to implement B2C PowerBI visualizations, the cost is considerably high especially for startups.
• Some clients are still resistant putting their data on the cloud, which restricts lots of functionality to Power BI.

Incentivized

Microsoft BI is fundamental to our suite of BI applications. That being said, Northcraft Analytics is focused on delighting our customers, so if the underlying factors of our decision change, we would choose to re-write our BI applications on a different stack. Luckily, mathematics are the fundamental IP of our technology... and is portable across all BI platforms for the foreseeable future.

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 Microsoft BI tools have great usability for both developers and end users alike. For developers familiar with Visual Studio, there is little learning curve. For those not, the single Visual Studio IDE means not having to learn separate tools for each component. For end-users, the web interface for SSRS is simple to navigate with intuitive controls. For ad-hoc analysis, Excel can connect directly to SSAS and provide a pivot table like experience which is familiar to many users. For database development, there is beginning to be some confusion, as there are now three tool choices (VS, SSMS, Azure Data Studio) for developers. I would like to see Azure Data Studio become the superset of SSMS and eventually supplant it.

Incentivized

The product has been reliable.

SQL Server Reporting Services (SSRS) can drag at times. We created two report servers and placed them under an F5 load balancer. This configuration has worked well. We have seen sluggish performance at times due to the Windows Firewall.

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

Incentivized

While support from Microsoft isn't necessarily always best of breed, you're also not paying the price for premium support that you would on other platforms. The strength of the stack is in the ecosystem that surrounds it. In contrast to other products, there are hundreds, even thousands of bloggers that post daily as well as vibrant user communities that surround the tool. I've had much better luck finding help with SQL Server related issues than I have with any other product, but that help doesn't always come directly from Microsoft.

This training was more directed toward what the product was capable of rather than actual programming.

I have used on-line training from Microsoft and from Pragmatic Works. I would recommend Pragmatic Works as the best way to get up to speed quickly, and then use the Microsoft on-line training to deep dive into specific features that you need to get depth with.

We are a consulting firm and as such our best resources are always billing on client projects. Our internal implementation has weaknesses, but that's true for any company like ours. My rating is based on the product's ease of implementation.

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

We have used the built in ConnectWise Manager reports and custom reports. The reports provide static data. PowerBI shows us live data we can drill down into and easily adjust parameters. It's much more useful than a static PDF report.

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

• As a SaaS provider we see being able to provide self-service BI to our client users as a competitive advantage. In fact the MSSQL enabled BI is a contributing factor to many winning RFPs we have done for prospective client organisations.
• However MSSQL BI requires extensive knowledge and skills to design and develop data warehouses & data models as a foundation to support business analysts and users to interrogate data effectively and efficiently. Often times we find having strong in-house MSSQL expertise is a bless.

Incentivized