Elasticsearch vs. Splunk Enterprise

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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I'm liking the newer products, and I'm looking forward to how they integrate with the overall product when they come together. Just log in and be able to query a large number of systems for similar issues or a unique one. That is a great fit for Splunk Enterprise, looking for a simple case or a simple String or something of that nature across multiple machines. It's a great fit for that to identify issues or particular software, whatever your scenario is, String, to find it across any particular server or group of servers, so that you can update or do a deployment or whatever it is you're looking to do.

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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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• It is very useful in creating custom rules for analyzing system logs and display relevant information. The query language is very easy to learn.
• We can create custom UI to visualize the output of our data. The interface is very flexible. It also allows the sharing of rules among users.
• There is an open online community to help others. Stackoverflow also has a splunk community. These resources make it more convenient to learn.

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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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• Splunk light limits number of users to 5. Wish there was a flexible license, where one could add more users.
• Splunk light does not let you add > few realtime alerts. Wish there was a flexible license, where one could add as many realtime alerts as wanted.
• Better insight into daily ingestion values

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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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We are using Splunk extensively in our projects and we have recently upgraded to Splunk version 6.0 which is quite efficient and giving expected results. We keep track of updates and new features Splunk introduces periodically and try to introduce those features in our day to day activities for improvement in our reporting system and other tasks.

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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You can literally throw in a single word into Splunk and it will pull back all instances of that word across all of your logs for the time span you select (provided you have permission to see that data). We have several users who have taken a few of the free courses from Splunk that are able to pull data out of it everyday with little help at all.

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When properly setup and configured, Splunk is extremely reliable.

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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 maintains a well resourced support system that has been consistent since we purchased the product. They help out in a timely manner and provide expert level information as needed. We typically open cases online and communicate when possible via e-mail and are able to resolve most issues with that method.

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The online course was simple clear and described the main capabilities of the solution. There is also an initial module that can be done for free so anyone can familiarize themselves with the functionality of this solution. On the other hand, however, there could be more free online courses. Maybe even with a certificate, this would broaden the group of people who are familiar with the platform while increasing familiarity with the solution itself.

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

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Smooth without too many major issues.

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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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A lot of products have natively inside their own dashboards and or their own logging repositories. And each one is difficult to learn or they're too complex or they're not verbose in the sense that they're not easy to mine the data that you're looking for. So that could be anything from the native logging that you find in other Cisco products. It's easier to use Splunk to draw the data that you're looking for as opposed to going to the individual's products themselves to get the logs that you're looking for.

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Splunk can scale in to the petabyte per day range which of course is awesome

• 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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• Splunk has allowed developers to diagnose production issues when access of control was taken away from them to be allowed to view items in production environments and I believe that is invaluable.
• At times some developers weren't super happy about using it, but it was more of the fact that they were used to having production access and not creating their splunk queries to get information.
• Going one place to view logs was very beneficial to have.

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