I used a different solution before switching to Elastic Search because Elastic Search offered a wider range of features. The other solution focused on monitoring app usage, Elastic Search stood out with its extensive modules, cloud deployment options, and flexible monitoring capabilities. Despite Splunk being a bigger name, I found Elastic Search to be more versatile and enjoyed using it.

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We did no previously use a different solution. 

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Our help desk also uses Grafana. We'll use this solution more widely eventually. 

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I have experience with Delinea, ManageEngine, BeyondTrust, IBM and WALLIX. But compared to Elastic, they lack the same level of artificial intelligence capabilities. It's like an all-encompassing package with tons of features. One of those features is the ability to pinpoint the root cause of any problem, whether it's code issues (like it was not written properly), developer errors, or anything else. It goes beyond just surface-level troubleshooting and digs deep to give you the real why. That's what sets it apart from the others. Imagine an application is having some issues. Elastic can tell if it's faulty code, a developer mistake, or anything else. It gives you the true root cause, not just the surface-level symptoms. That's its strength and why it stands out as the industry standard. View full review »

I have not used other similar solutions to Elastic Enterprise Search.

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I have used Prometheus and Grafana. They do not offer the capabilities of ELK and their focus is different.

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Yes, we had a previous solution but we did not switch. We use multiple log analysis engines. Where we have funds to support commercial, off-the-shelf tools (COTS), we have seen more immediate benefits. Where we must go with low/no-cost FOSS, we use ELK.

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I previously used Stackdriver. 

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I'm also familiar with Splunk, which is more expensive.

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I have worked with all the flavours of Elasticsearch viz. Elastic.co's ELK which is popularly known as the ELK stack (pronounced as 'yelk'), AWS Elasticsearch and Open Distro plugins for Elasticsearch.

All (including Solr that comes with Hadoop) are built on a common underlying technology, Apache Lucene. The difference is the added features that I call 'batteries included'. To be precise, Elastic's ELK Elasticsearch, unlike others, comes with free enterprise-grade apps (called plugins in Kibana) and a bunch of cool and useful Kibana features. It also features a good deal of engineering automation conveniences built into the stack.

Did you know that the original founders of Elasticsearch are the folks at Elastic.co, the company that has recently transitioned to an open-core philosophy by design. But since AWS took the initial lead and started offering the stack as AWS Elasticsearch service it became more popular and a preferred option for the uninformed. Elastic, on the other hand, was busy innovating and adding more muscle to the stack that it is no more limited to being just the fastest search engine on the planet. In fact, the keyword ‘search’ in Elasticsearch is not relevant anymore and, moreover, it is misleading.

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I have experience working with Splunk in the past.

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We chose Elasticsearch because we could build a model in a short amount of time. It allows us to build a whole setup in one month and get 93% accuracy. Even if you look at the complex AI-based features that we built within a shorter span, we could build that model with high accuracy, which wasn't possible with other search enterprise vendors that we used.

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We switched from SQL Server to Elasticsearch. For our application, we wanted the information very fast without locking everything. In SQL Server or Oracle, that would not have been possible. Deleting is also very difficult in SQL Server.

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Before we switched over to this, we used it in combination with an end product called QRadar, but both of them together were time-consuming. 

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I have not worked with another solution.

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Before ELK, we used another solution for internal usage, and also, we used Splunk for different use cases in a different organization altogether.

It wasn't a switch per se, it was a different organization with a different use case.

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I have used SolarWinds in the past.

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I previously provided some activities with Splunk solutions. My colleague in another department also used Splunk.

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We used Logentries. Because of open source we moved to ELK, considering it as part of a cost-cutting strategy and evaluation. But due to the lack of a notification feature, we again moved to Logentries.

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We cannot disclose the previous solution, but we are much happier with Elasticsearch.

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I haven't used other solutions. 

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We did not use another solution prior to this one.

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Datadog, it's expensive when it comes for a big infrastructure and cannot be self hosted when it comes to specific sensitive cases.

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We did have a previous solution. It was a monster to install and configure.

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