Apache Spark vs. Microsoft SQL Server

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Microsoft SQL Server
Score 8.6 out of 10
N/A
Microsoft SQL Server is a relational database.
$1,418
Per License
Pricing
Apache SparkMicrosoft SQL Server
Editions & Modules
No answers on this topic
Subscription
$1,418.00
Per License
Enterprise
$13,748.00
Per License
Offerings
Pricing Offerings
Apache SparkMicrosoft SQL Server
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkMicrosoft SQL Server
Top Pros
Top Cons
Best Alternatives
Apache SparkMicrosoft SQL Server
Small Businesses

No answers on this topic

SingleStore
SingleStore
Score 9.0 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.7 out of 10
SAP IQ
SAP IQ
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkMicrosoft SQL Server
Likelihood to Recommend
10.0
(23 ratings)
9.9
(97 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.0
(6 ratings)
Usability
10.0
(3 ratings)
9.0
(11 ratings)
Availability
-
(0 ratings)
10.0
(1 ratings)
Performance
-
(0 ratings)
9.0
(1 ratings)
Support Rating
8.7
(4 ratings)
7.9
(25 ratings)
In-Person Training
-
(0 ratings)
9.0
(1 ratings)
Online Training
-
(0 ratings)
9.0
(1 ratings)
Implementation Rating
-
(0 ratings)
9.0
(5 ratings)
Configurability
-
(0 ratings)
10.0
(1 ratings)
Ease of integration
-
(0 ratings)
9.0
(1 ratings)
Product Scalability
-
(0 ratings)
9.0
(1 ratings)
Vendor post-sale
-
(0 ratings)
9.0
(1 ratings)
Vendor pre-sale
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Apache SparkMicrosoft SQL Server
Likelihood to Recommend
Apache
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.
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Microsoft
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.
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Pros
Apache
  • 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
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Microsoft
  • 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
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Cons
Apache
  • 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
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Microsoft
  • 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
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Microsoft
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.
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Usability
Apache
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.
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Microsoft
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.
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Reliability and Availability
Apache
No answers on this topic
Microsoft
Its does not have outages.
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Performance
Apache
No answers on this topic
Microsoft
SSAS data cubes may some time slow down your Excel reports.
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Support Rating
Apache
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.
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Microsoft
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.
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In-Person Training
Apache
No answers on this topic
Microsoft
It was good
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Online Training
Apache
No answers on this topic
Microsoft
very hands on and detailed training
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Implementation Rating
Apache
No answers on this topic
Microsoft
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.
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Alternatives Considered
Apache
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.
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Microsoft
[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.
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Scalability
Apache
No answers on this topic
Microsoft
SQL server does handle growing demands of a mid sized company.
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Return on Investment
Apache
  • 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
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Microsoft
  • 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.
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