Elasticsearch vs. Splunk Cloud Platform

Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.

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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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• As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
• Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
• Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.

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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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• Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
• Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
• Schema changes require complete reindexing of an index

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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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We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

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Ease of use and have all the features we need

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To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.

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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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We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.

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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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Do not mix data and master roles. Dedicate at least 3 nodes just for Master

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As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.

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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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• We have had great luck with implementing Elasticsearch for our search and analytics use cases.
• While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
• We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.

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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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