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 A simple and important scenario well suited is that you can configure alerts to notify you when the production server fails. another best feature is the report server is the central component of reporting services. For me something less appropriate is that the admin must ensure optimal performance for farm operations, they recommend that you install SQL Server on a dedicated server that does not run other farm roles and does not host databases for other applications.
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 Flexible - able to make any changes we would like vs traditional service desk system. ROI - We were already using SharePoint for internal intranet, so we are simply getting more use out of licensing we had already committed to. Easy to use for end users. 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 It is hard to setup and nightmare It requires a of infrastructure, thus it could be costly because of requirement and licensing required for everything to run smoothly If it is not setup and organized properly from the beginning it could be maintenance nightmare It is hard to have "test" environment to do patches or similar Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
Read full review This was a long-term buy-in from a corporate perspective, to remain in the SharePoint space. Migration is certainly possible, which is good for planning and having options further out. At this point, the only planned migration is to eventually move the architecture up to SharePoint/SQL 2013. At that point, we will be able to leverage some greater efficiencies, some enhanced content design and management features, and some more current social features. It is well worth a full consideration in any shop looking at a new implementation of or migration to SharePoint (although you will probably be considering 2013 versions or beyond in those discussions), but the platform should be a strong competitor to any alternatives. Realizing the capability of a fully-branded and customized website was not part of the original choice for the architecture at Lincoln, but seeing it implemented and functioning now with this capacity far beyond original expectations has certainly cemented plans to continue using it.
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 SharePoint is very complex. This makes usability somewhat difficult from an IT perspective. An IT generalist will be able to pick it up and run with basic tasks. More customized functions would require significant specialized training and therefore limit what a standard user would be able to achieve. From an end user perspective, it's pretty straightforward to use.
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 It's been fantastic in terms of Premier Support so far. If there is an issue and if you report if the product has an issue, they will act upon it immediately. In some cases, if you design/develop something using the platform, Microsoft appreciates it and... publishes it on their public website. But you have to wait for some time if it is a non-Premier Support issue as you may experience delays.
Read full review Implementation Rating Not implemented in best practice way, there are many customizations
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 At the time of the two large projects, SharePoint was the enterprise solution so we were required to use that. We have since lobbied the enterprise teams to review and consider
Atlassian Confluence and were successful.
Confluence is cheaper than Sharepoint which is why we wanted to bring that in. The enterprise has now made
Confluence an enterprise solution as an alternative to SharePoint. After using both I think SharePoint has many more add-ins than
Confluence . It has much more customization ability than
Confluence . SharePoint is not good for mobile readiness.
Confluence is so there is a difference that might lead you to
Confluence over SharePoint. I would also say that SharePoint is very document-centric and that
Confluence has better KM than SharePoint does. even with the use of SQL Server. We were told that we could not use
Google Drive even though it had features we liked.
Gene Baker Vice President, Chief Architect, Development Manager and 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 I've installed SharePoint in many different industries and each industry has seen greater collaboration among their teams both locally and nationally. The ability to collaborate more efficiently has reduced the need to have employees centrally located. Companies which have used SharePoint in a end user training portal have had great ROI, since they can create the content once and share with all their users who subscribe to their training service. The web content management aspect of SharePoint is a very helpful feature. Read full review ScreenShots