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I'm working on a system that performs daily HPC on a computer grid. There are a lot of servers, service and applications (Windows,Linux,IIS,Apache,Websphare,SQL,Mongo,.Net,Python,C++)
It runs once every day.
I'm very familiar with ELK. I especially like the way elastic works as a DB and its features: percolator, scoring system, Rest API, etc..
I've recently heard of Splunk, I've looked into it and it seems pretty nice.
Lets put the $ cost aside
What will be a better solution for a non-cloud base solution that creates a huge amount of logs for about 4 hours a day and may have stress peeks during that time?
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Both solutions are relatively easy to deploy and use, especially considering each respective platform's breadth of features and capabilities. That said, Splunk's dashboards offer more accessible features and its configuration options are a bit more refined and intuitive than ELK/Elastic Stack's.
Essentially, Splunk is a single closed-source product, whereas ELK combines the power of three open-source products – ElasticSearch, LogStash, and Kibana.
Elasticsearch stores the data and analyzes them, whereas Splunk is used to search, monitor, and analyze the machine data. One of the biggest disadvantage of Splunk till now is it is a paid and expensive tool whereas Elasticsearch is a free tool.
Both Splunk and ELK have many similar features such as search capabilities, reporting, alerts and data visualisation. When it comes to set-up and maintenance, as Splunk is a proprietary software, this is easier to configure and set up than the ELK Stack.
Let me summarise it for you:
Splunk - It's a very powerful, mainly on premises solution, expensive and, if all you want is "supergrep" then it's really too expensive for that use case.
ELK - License fees not incurred, but you'll end up spending $ on building various features, either right away if you already know you need N features or incrementally and over time as users start demanding features that ELK doesn't have.
Based on your requirement, i would suggest you to go with splunk.
P.S. If you want to look at detailed comparison, please have a look at this post.
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