Apache Spark vs. Microsoft SQL Server

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.

Incentivized

Microsoft SQL Server is ideal for highly available SQL workloads by using SQL Server Always On availability groups. Microsoft SQL Server might not be appropriate for solutions which require a very low resource footprint, since it requires significant CPU cores and RAM memory as well as high IOPS, always depending on the usage scenario.

Incentivized

• It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
• It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
• Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.

Incentivized

• Easy to configure and use with Visual Studio and Dot Net
• Easy integration with MSBI to perform data analysis
• Data Security
• Easy to understand and use
• Very easy to export database and tables in the form of SQL query or a script

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 import/export process can be tricky to follow with lots of steps and could be better for importing flat files
• Obtaining help from Microsoft is cumbersome and often other internet sources are better and quicker
• The documentation is not great and again it's generally better to obtain help elsewhere if needed

Incentivized

Capacity of computing data in cluster and fast speed.

I think it is unlikely that sql server has disappointed someone, it is likely that someone will come initially discouraged if the needs and problems that occur are very challenging, but definitely have a SQL oriented system means having a solid base to work and on which maintain the their data securely

Incentivized

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

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 good

Incentivized

very hands on and detailed training

Incentivized

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

We used Surprise Kit for one of the other research works. It is more fine-tuned to Recommendation systems and their algorithms. Apache Spark has MLlib for majority of ML problems. Where as software like Surprse Kit - it suitable for a specific task of Recommendations only

Incentivized

Microsoft SQL is slower than MySQL and Access but far more feature-rich and reliable. Access is almost obsolete nowadays, so not too many people are considering it, but unless budget or an open-source ethos is a factor, Microsoft SQL is superior in every way. Many commonly used tools, like Crystal Reports, support it.

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