Cloudera Data Science Workbench (discontinued) vs. Splunk Cloud Platform

Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.

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Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.

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• One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
• For larger organizations/teams, it lets you be self reliant
• As it sits on your cluster, it has very easy access of all the data on the HDFS
• Linking with Github is a very good way to keep the code versions intact

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• This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows.
• Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics.
• Splunk Cloud enables me to create and schedule statistical reports on network use for Management.

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• Installation is difficult.
• Upgrades are difficult.
• Licensing options are not flexible.

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• The SPL programming language that the queries are built in is not very intuitive.
• There should be a better repository of pre-built queries for what I would think of as common Active Directory usage monitoring.
• I would like to see more free training/familiarization information made available.

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Ease of use and have all the features we need

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Splunk AI Assistant for SPL is good tool, as I said, it is really useful for educational purpose, taking in its hands small and simple tasks (ex:small query dashboard,...), but it's not a ten because the contextual memory for complex issues/dashboarding/reports etc can be limited, and can sometime suggets none optimized query which are not always checked by teammates and can cause issue on the long term.

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Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.

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Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.

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Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.

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Vs elastic: - SPL's statistical functions superior to KQL for complex correlations - Better enterprise support, less operational overhead - Splunk Cloud Platform's detection engineering more mature vs Sentinel: - SPL more powerful than KQL for custom analytics - Better cross-platform visibility (non-Azure environments) vs Datadog - SPL enables deeper forensic Investigations - Superior compliance/audit capabilities

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• Paid off for demonstration purposes.

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• End-end visibility across your departmental silos
• Strengthen the overall global monitoring posture
• Move from Reactive to Proactive Monitoring
• Highly secure environment at your finger-tips
• Takes you away from managing infrastructure/administration, allows saving time & money. Reduce the overall TCO (Total Cost of Ownership)

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