Apache Spark vs. SAP Integration Suite

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 9.0 out of 10
N/A
N/AN/A
SAP Integration Suite
Score 8.6 out of 10
N/A
SAP Integration Suite is an integration platform-as-a-service (iPaaS) that helps quickly integrate on-premises and cloud-based processes, services, applications, events, and data. It is used to accelerate innovation, automate more processes, and realize a faster time to value.
$11,199
per year
Pricing
Apache SparkSAP Integration Suite
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkSAP Integration Suite
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsAccess to free tier services does not expire while there is an active Pay-As-You-Go or CPEA account with SAP. Once a free tier service limit has been reached users have the option to update from a free to a paid service plan in the same account.
More Pricing Information
Community Pulse
Apache SparkSAP Integration Suite
Top Pros
Top Cons
Features
Apache SparkSAP Integration Suite
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
Apache Spark
-
Ratings
SAP Integration Suite
7.8
158 Ratings
3% below category average
Pre-built connectors00 Ratings8.2147 Ratings
Connector modification00 Ratings7.8130 Ratings
Support for real-time and batch integration00 Ratings8.3144 Ratings
Data quality services00 Ratings7.7136 Ratings
Data security features00 Ratings8.0143 Ratings
Monitoring console00 Ratings6.9145 Ratings
Best Alternatives
Apache SparkSAP Integration Suite
Small Businesses

No answers on this topic

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Score 9.3 out of 10
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Score 9.3 out of 10
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IBM Analytics Engine
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User Ratings
Apache SparkSAP Integration Suite
Likelihood to Recommend
9.3
(24 ratings)
8.5
(262 ratings)
Likelihood to Renew
10.0
(1 ratings)
8.6
(3 ratings)
Usability
8.6
(4 ratings)
8.0
(2 ratings)
Support Rating
8.7
(4 ratings)
6.0
(4 ratings)
Implementation Rating
-
(0 ratings)
7.3
(1 ratings)
User Testimonials
Apache SparkSAP Integration Suite
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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SAP
The case of the SAP Integration Suite shows outstanding capability in handling huge data, and especially when handling corporate portfolio data with multiple owners. But we have noticed that in the cases of moving data from the older banking system, it is necessary to further refine the system. It does an excellent job of the current credit scoring.
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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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SAP
  • Broad Connectivity: SAP Integration Suite excels in connecting diverse systems, including SAP and non-SAP applications, databases, and third-party services.
  • Pre-built Integrations and Templates:The suite provides a library of pre-built integrations and templates for common business scenarios.
  • User-Friendly Design Tools:
  • API Management:
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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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SAP
  • Provide more pre-built integrations to use within SuccessFactors or other modules instead of everything having to be custom built
  • Support is unable to provide advice on custom builds so you often have to engage a 3rd party partner
  • Works best when you have the functional and technical teams working together. Otherwise, the system is too technical for a functional user to create integration and a technical user not always understand the functional perspective
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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SAP
SAP Integration Suite is very helpful to us in many ways to manage purchase procedures, stocks, and data of vendors and suppliers. Also, it helps to manage data for service providers. SAP Integration Suite has the tool to conduct training and evaluation. Unique features like the cloud can provide access from any place and by any device is very helpful.
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Usability
Apache
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
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SAP
It's very easy to use and configure.
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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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SAP
Support is hard to reach and takes a long time to get a response.
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Implementation Rating
Apache
No answers on this topic
SAP
Work with an SAP implementation partner, if possible.
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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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SAP
SAP Integration Suite was already part of our SAP stack, part of Business Technology Platform, with out-of-the-box integration with S/4 HANA transactional and ERP system that we are using as our main back-end. Thus, we are achieving significant Total Cost Optimization benefits or running both solutions on the same platform, hosted on Azure cloud.
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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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SAP
  • In the long run will enable us to undeploy our on premise solution
  • As long as integration suite doesn't support all capabilities PO has complete migration is hard
  • Will probably be cheaper than our on premise solution when we are completely migrated
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ScreenShots

SAP Integration Suite Screenshots

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