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I know it is just one company example, but can you elaborate on this point?

Is it more about features absent in estc or that price being equal the splk features are better?


For us, it came down to:
• In Elastic we had to define each field in advance during upload while Splunk is just a simple upload.
• With Splunk, our users can do dynamic data discovery/analysis/exploration. This is what Splunk does best. Chart X against Y. See a trend or a blip. Chart Z against Y. Or, find nodes where X and Z are both maxed out/correlated to each other and then deep dive into those few nodes.
• Our developers like Splunk's query language better. The output of one query can be piped into another query, so you can build up analyses from building blocks.
ª The output of Kibana wasn't as rich graphically as we got from Splunk, and the interactive nature of Splunk's output generation was really the main differentiator for us.
• Now, this may be old. When I last looked, the Elastic Stack (ELK then) was a collection of separate tools (Elasticsearch, Logstash and Kibana). It appears that Elastic has put a bunch of effort into improving them, and just this year released some new UI for Kibana. So, it's possible that my current team may make a different choice, but their thinking going in is to use Splunk.

Finally, remember, that Splunk started as a way to make sense of the ton of data coming from server logs. If you have a cluster of servers (dozens or hundreds) and something goes wrong you don't want to have to look at each server's log independently. Splunk was originally made to suck in all the logs and then help you find which nodes were not performing well, and then determining why. My IoT use cases are quite a bit different - we have orders of magnitude more nodes, but orders of magnitude less data from each node.
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