Apache Spark vs. SAP Vora

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.5 out of 10
N/A
N/AN/A
SAP Vora
Score 6.0 out of 10
N/A
SAP Vora is a computing engine designed to provide better accessibility to Hadoop data from SAP HANA. SAP Vora manages unstructured Hadoop data by building structured data hierarchies and making the data queryable through an SQL interface.N/A
Pricing
Apache SparkSAP Vora
Editions & Modules
No answers on this topic
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Offerings
Pricing Offerings
Apache SparkSAP Vora
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
Best Alternatives
Apache SparkSAP Vora
Small Businesses

No answers on this topic

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Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkSAP Vora
Likelihood to Recommend
9.4
(23 ratings)
6.0
(1 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
9.4
(2 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
-
(0 ratings)
User Testimonials
Apache SparkSAP Vora
Likelihood to Recommend
Apache
The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
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SAP
I spent more than 1 year with SAP Vora, SAP Datahub and SAP Leonardo with ML, iOt. I believe this product has potential but it is not easy to adopt. SAP has to keep in mind how open-source big data technologies are able to deliver quick results. I know SAP is stabilizing and fighting hard against many open source technologies, but it still has a long way to go there.
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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
  • Modelling with SAP HANA and Hadoop
  • Realtime Analysis using Vora and HANA as a Streaming engine
  • Time series Analysis on large chunks of datasets
  • Machine learning capabilities on Hadoop tables and spark contexts
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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
  • Vora 2.0 in on premise scenarios could be improved, as adoption of the cloud is not an easy sell.
  • Kubernetes and Docker integration need to be more seamless and quick to understand. If this is simplified, it will be easy to adopt
  • Data hub orchestration and integrations could be simplified so that quick adoption within SAP BW, ECC, S4 HANa scenarios is possible.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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SAP
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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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SAP
No answers on this topic
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
No answers on this topic
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
No answers on this topic
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
  • Negative impact would be Poc and RFI will need more time to adopt and decision making gets delayed
  • Positive impact would be it's a great leap from SAP to adopt a Big data technologies and AI within cloud stream. But selling is going to take time.
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