Apache Spark vs. Microsoft BI (MSBI)

Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.

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.

• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

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

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

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

Capacity of computing data in cluster and fast speed.

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.

If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used

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.

1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.

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.

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.

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

• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

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