Apache Spark vs. SQL Server Integration Services

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.8 out of 10
N/A
N/AN/A
SSIS
Score 8.0 out of 10
N/A
Microsoft's SQL Server Integration Services (SSIS) is a data integration solution.N/A
Pricing
Apache SparkSQL Server Integration Services
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkSSIS
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 SparkSQL Server Integration Services
Top Pros
Top Cons
Features
Apache SparkSQL Server Integration Services
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Apache Spark
-
Ratings
SQL Server Integration Services
7.5
53 Ratings
11% below category average
Connect to traditional data sources00 Ratings8.853 Ratings
Connecto to Big Data and NoSQL00 Ratings6.240 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Apache Spark
-
Ratings
SQL Server Integration Services
8.1
53 Ratings
2% below category average
Simple transformations00 Ratings8.553 Ratings
Complex transformations00 Ratings7.752 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
SQL Server Integration Services
7.4
51 Ratings
8% below category average
Data model creation00 Ratings8.627 Ratings
Metadata management00 Ratings7.133 Ratings
Business rules and workflow00 Ratings8.142 Ratings
Collaboration00 Ratings7.338 Ratings
Testing and debugging00 Ratings6.148 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
Apache Spark
-
Ratings
SQL Server Integration Services
6.9
41 Ratings
18% below category average
Integration with data quality tools00 Ratings7.436 Ratings
Integration with MDM tools00 Ratings6.436 Ratings
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Apache SparkSQL Server Integration Services
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User Ratings
Apache SparkSQL Server Integration Services
Likelihood to Recommend
10.0
(23 ratings)
8.0
(53 ratings)
Likelihood to Renew
10.0
(1 ratings)
10.0
(3 ratings)
Usability
10.0
(3 ratings)
9.3
(8 ratings)
Performance
-
(0 ratings)
8.8
(6 ratings)
Support Rating
8.7
(4 ratings)
8.2
(7 ratings)
Implementation Rating
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Apache SparkSQL Server Integration Services
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
Ideal for daily standard ETL use cases whether the data is sourced from / transferred to the native connectors (like SQL Server) or FTP. Best if the company uses MS suite of tools. There are better options in the market for chaining tasks where you want a custom flow of executions depending on the outcome of each process or if you want advanced functionality like API connections, etc.
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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
  • Ease of use - can be used with no prior experience in a relatively short amount of time.
  • Flexibility - provides multiple means of accomplishing tasks to be able to support virtually any scenario.
  • Performance - performs well with default configurations but allows the user to choose a multitude of options that can enhance performance.
  • Resilient - supports the configuration of error handling to prevent and identify breakages.
  • Complete suite of configurable tools.
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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
  • SSIS has been a bit neglected by Microsoft and new features are slow in coming.
  • When importing data from flat files and Excel workbooks, changes in the data structure will cause the extracts to fail. Workarounds do exist but are not easily implemented. If your source data structure does not change or rarely changes, this negative is relatively insignificant.
  • While add-on third-party SSIS tools exist, there are only a small number of vendors actively supporting SSIS and license fees for production server use can be significant especially in highly-scaled environments.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Microsoft
Some features should be revised or improved, some tools (using it with Visual Studio) of the toolbox should be less schematic and somewhat more flexible. Using for example, the CSV data import is still very old-fashioned and if the data format changes it requires a bit of manual labor to accept the new data structure
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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 Integration Services is a relatively nice tool but is simply not the ETL for a global, large-scale organization. With developing requirements such as NoSQL data, cloud-based tools, and extraordinarily large databases, SSIS is no longer our tool of choice.
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Performance
Apache
No answers on this topic
Microsoft
Raw performance is great. At times, depending on the machine you are using for development, the IDE can have issues. Deploying projects is very easy and the tool set they give you to monitor jobs out of the box is decent. If you do very much with it you will have to write into your projects performance tracking though.
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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
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
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Implementation Rating
Apache
No answers on this topic
Microsoft
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
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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
I had nothing to do with the choice or install. I assume it was made because it's easy to integrate with our SQL Server environment and free. I'm not sure of any other enterprise level solution that would solve this problem, but I would likely have approached it with traditional scripting. Comparably free, but my own familiarity with trad scripts would be my final deciding factor. Perhaps with some further training on SSIS I would have a different answer.
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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
  • Data integrity across various products allows unify certain processes inside the organization and save funds by reducing human labour factor.
  • Automated data unification allows us plan our inputs better and reduce over-warehousing by overbuying
  • The employee number, responsible for data management was reduced from 4 to 1 person
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