What users are saying about
109 Ratings
2 Ratings
109 Ratings
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Score 8.4 out of 101
2 Ratings
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Score 8 out of 101

Likelihood to Recommend

Apache Spark

Spark is great as a workflow process and extract transform layer process tool. Is really good for machine learning especially for large datasets that can be processed in split file paralallelization. Spark streaming is scalable for close to real-time data workflow process.what it's not good for, is smaller subset of data processing.
Anson Abraham profile photo

SAP Vora

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.
Dhinesh Kumar Ganeshan,PMP,CSM profile photo

Pros

Apache Spark

  • Machine Learning.
  • Data Analysis
  • WorkFlow process (faster than MapReduce).
  • SQL connector to multiple data sources
Anson Abraham profile photo

SAP Vora

  • 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
Dhinesh Kumar Ganeshan,PMP,CSM profile photo

Cons

Apache Spark

  • 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
Anson Abraham profile photo

SAP Vora

  • 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.
Dhinesh Kumar Ganeshan,PMP,CSM profile photo

Alternatives Considered

Apache Spark

vs MapRedce, it was faster and easier to manage. Especially for Machine Learning, where MapReduce is lacking. Also Apache Storm was slower and didn't scale as much as Spark does. Spark elasticity was easier to apply compared to storm and MapReduce.managing resources for Spark was easier compared to storm as well. MapReduce is slower than spark.
Anson Abraham profile photo

SAP Vora

We selected SAP VORA because we needed acclerated integration with different sources with a huge amount of data. Also the data de-duplication has easily eliminated the different entries in a fastest and enhanced way, which ultimately leads us and the customer to prefer SAP Vora against different products, and has helped eliminate any limitations in using and playing with our data lake.
Pradeep Bele profile photo

Return on Investment

Apache Spark

  • We saved a lot of time and resources, thereby saving a lot of dollars for our company as well as the client.
No photo available

SAP Vora

  • 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.
Dhinesh Kumar Ganeshan,PMP,CSM profile photo

Pricing Details

Apache Spark

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

SAP Vora

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

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