Likelihood to Recommend 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.
Read full review In operations we use the tool for many different topics, from factory quality systems to high level reviews. We have created kind of an internal "App Store" based on Power BI where you have a lot of different dashboards for different solutions (cost, cash, health and safety, sales, factories, distribution centers...) and you as an user just need to get in that "App Store" and enter in whatever tool can be useful for you. It is open to all the operations employees and can use on demand. Also it has raised the imagination of our colleagues, as they are not only working by themselves creating new reports, but also raising fantastic ideas that can be extended for the usage of all the community.
Read full review Pros Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner. Apache Spark does a fairly good job implementing machine learning models for larger data sets. Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use. Read full review Quickly filter and view granular data sets with easily configurable reporting figures. The ability to quickly switch between tabs allows for historical data comparisons and progress tracking. It's helpful to dive into the data and break it down from high levels to small. Achieve actionable insights faster by using real-time data. It's incredible to have online access to reports. Read full review Cons 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 Read full review The desktop app is great but needs a lot of performance improvements No MacOS Version for the Desktop app, this is a big limitation for business since executives prefer Macs Premium Cloud Version of Power BI is awfully expensive On-Premise Version of the Power BI Reports Server is bundled only with SQL Server Enterprise License and cannot be purchased separately and requires Software Assurance Subscription On-Premise Power BI Report Server doesn't support ADFS, AzureAD or any Claims-Based authentication platform, a sad disadvantage for enterprises Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
Read full review I find it helpful and easy to use
Read full review Usability The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
Read full review At this point, I think we all know who has taken the lead in the business intelligence and analytics market worldwide. With fresh new updates every other day on top of an already robustly built product with all features that one can dream of is a no brainer, I feel. Microsoft will invariably be synonymous with quality and professionalism.
Read full review Support Rating 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.
Read full review I can't really speak to the support overall, [but] I will say that in the almost three years I have used the system, I have only needed to contact their support team once. I think the team was helpful, but it did take some time for us to resolve the issues/ request that they had. I guess the good news is that the system is pretty stable, and I personally have rarely needed to contact their technical support team.
Greg Watkins Senior Customer Success Manager (Strategic Accounts)
Read full review Alternatives Considered All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like
Presto . Combining it with Jupyter Notebooks (
https://github.com/jupyter-incubator/sparkmagic ), one can develop the Spark code in an interactive manner in Scala or Python
Read full review [Microsoft] Power BI is practical and effective, like a hammer for a nail, it is easy to use and produces very quickly the results that in most cases are urgently required by clients (nice reports to share on the web). To start using [Microsoft] Power BI you need a business email address, with that you create an account in Power BI Service and in less than 1 hour you will have installed Power BI Desktop, a report will have been created and it will have been published on the web .
Dustin Ghia Lead Consultant - Solutions Architect - Software Engineer
Read full review Return on Investment Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark. Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy. Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs. Read full review It helps us track and achieve company goals. It shows us [our] performance in areas we could not previously track and allows us to see and work on how to improve their performance. Quick easy access for executives to use in helping teams and addressing issues. Read full review ScreenShots Microsoft Power BI Screenshots