Cloudera Distribution Hadoop (CDH) vs. SAP Vora

Cloudera Distribution Hadoop (CDH) does a lot of things really well - especially on the analytical front. That being said the product is quite expensive. There are seemingly numerous applications that do the same thing on the functional level that are much more cost effecient for enterprise teams. If I were recommending this to a colleague I would let them know the product will absolutely be able to get the job done for their use case, but there are more efficient options

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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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• Solid and robust set of integrations
• Easy to use and easy to deploy across the enterprise
• Reliability - never lost any info
• Simple and clean interface

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• 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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• The price is quite high competitively speaking
• Hard to learn more robust functions and custom options without experience

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• 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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In terms of functionality there's not much difference, both get the job done. Amazon was more cost-efficient for our team, but this could vary depending on the size of the business. One thing I did notice was that Cloudera seemed to management and spit out our deployments faster than AWS.

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• Saves time by automating typically manual processes (data management, lifecyle AI etc)
• Quick deployments and analytics allow for faster time-to-value

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• 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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