159 Reviews and Ratings
1613 Reviews and Ratings
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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 SQL Server is a great RDBMS and meets all of our requirements. If you need a stable DB platform to support your line of a business application you'll be well served. Licensing costs are far cheaper, more portable and a lot more user friendly than Oracle. Product support and security patches from Microsoft are strong.Incentivized
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.Incentivized
Easy to configure and use with Visual Studio and Dot NetEasy integration with MSBI to perform data analysisData SecurityEasy to understand and useVery easy to export database and tables in the form of SQL query or a scriptIncentivized
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 localityIncentivized
The import/export process can be tricky to follow with lots of steps and could be better for importing flat filesObtaining help from Microsoft is cumbersome and often other internet sources are better and quickerThe documentation is not great and again it's generally better to obtain help elsewhere if neededIncentivized
Capacity of computing data in cluster and fast speed.
We understand that the Microsoft SQL Server will continue to advance, offering the same robust and reliable platform while adding new features that enable us, as a software center, to create a superior product. That provides excellent performance while reducing the hardware requirements and the total cost of ownership of our solution.Incentivized
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.Incentivized
SQL Server mostly 'just works' or generates error messages to help you sort out the trouble. You can usually count on the product to get the job done and keep an eye on your potential mistakes. Interaction with other Microsoft products makes operating as a Windows user pretty straight forward. Digging through the multitude of dialogs and wizards can be a pain, but the answer is usually there somewhere.Incentivized
Its does not have outages.Incentivized
SSAS data cubes may some time slow down your Excel reports.Incentivized
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.
We managed to handle most of our problems by looking into Microsoft's official documentation that has everything explained and almost every function has an example that illustrates in detail how a particular functionality works. Just like PowerShell has the ability to show you an example of how some cmdlet works, that is the case also here, and in my opinion, it is a very good practice and I like it.Incentivized
It was goodIncentivized
very hands on and detailed trainingIncentivized
Other than SQL taking quite a bit of time to actually install there are no problems with installation. Even on hardware that has good performance SQL can still take close to an hour to install a typical server with management and reporting services.Incentivized
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 Incentivized
[Microsoft] SQL Server has a much better community and professional support and is overall just a more reliable system with Microsoft behind it. I've used MySQL in the past and SQL Server has just become more comfortable for me and is my go to RDBMS.Incentivized
SQL server does handle growing demands of a mid sized company.Incentivized
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.Incentivized
Increased accuracy - We went from multiple users having different versions of an Excel spreadsheet to a single source of truth for our reporting.Increased Efficiency - We can now generate reports at any time from a single source rather than multiple users spending their time collating data and generating reports.Improved Security - Enterprise level security on a dedicated server rather than financial files on multiple laptop hard drives.Incentivized