Elastic Search Reviews

In terms of use case, we combine a lot of things with Elastic. It's two platforms, so with Elasticsearch, we're using the Beats, Kibana, and Suricata. It's a query engine and we use the information from our sensors. It gets ingested into that and we use the resources to get everything put on our dashboards. If something is detected, alerts come up right away and it's very, very accurate. The more ingest it receives, the better we can respond to threats. It's not just Elastic or Logstash, it's a combination of those and other tools that we would apply towards our threat detection and prevention. We have a partnership with ELK.

The company provides excellent technical support and wonderful engineers, even their sales engineers are great. The dashboard is a valuable feature - it's awesome and very customizable. 

I would like to see more open source tools and testing as well as a signature analysis in the solution. I think that a lot of times when we go into a corporate environment where it becomes more add on features or an additional service fee, it typically draws away from that product. 

I think it would be cool if they could provide a couple of licenses that would be test bed licenses so that engineers and people with have their hands on the keyboard could test any new development. 

I've been using this solution for three or four years. 

It is a very scalable soluton. It is very easy and I would recommend it to anyone. In terms of users it's all tiered. Most things are from tier zero at egress point of any major large-scale network all the way down to the customer. We have roughly 200 users. And those would include analysts and real time threat analysts. 

I'm very satisfied with the technical support and would rate it highly. Sometimes there are issues because we are overseas and there is a six hour time difference which creates a lag. It's hard to get around that but they're very responsive. 

We had issues when we first did the initial setup, because our resources were limited because it was a test that it was a proof of concept. It meant the initial setup was somewhat resource intensive. The data NGS itself was an issue when we were trying to filter and pull that information. Again, a signature analysis would have been helpful here.

For anyone considering implementing this solution, I would say take a good hard look at your own infrastructure resources and scalability as you have to future proof everything. Whether it's scale or increase in customers building up through your actual hardware and your network infrastructure. You need to know it's capable of performing the tasks needed, because sometimes you outgrow yourself. So, I would say look at your resources and how it can be scaled.

I would rate this solution a nine out of 10. 

We use Elasticsearch as an alternative to Splunk. It is basically for log monitoring.

It's probably a cost-efficient alternative to Splunk. The search interface is nearly the same. When it comes to visualizations, Elastic is a bit better than Splunk.

Elastic Search needs better guides for developers. Better guides for development.

I have been using it for a year.

I would rate the stability an eight out of ten. 

It's fairly scalable. I would rate the scalability of this solution a ten out of ten. 

There are around five end users using it in my team. 

Till date, we did not have any issues with  customer service and support. Like, initially, we had issues in accessing the portal. But that was the only issue, but it was resolved pretty quick.

The initial setup is fairly simple. Initially, it was on-prem, but right now, it's on the cloud.

It is pretty easy to integrate as well.

It's like, when someone is buidling products for scale, it reduces the time to market.

I would rate the pricing a seven out of ten, with one being high price and ten being low price. It could be cheaper for certain use cases, but since it gets the job done, no complaints for the pricing. 

Overall, I would rate it a nine out of ten. I would definitely recommend it to other users. 

The tool's stability and performance are good. 

Elastic Search needs to improve its technical support. It should be customer-friendly and have good support. 

I have been using the product for a year. 

The tool is stable; I rate it an eight to nine out of ten. 

The product is scalable, and I rate it a ten out of ten. My company has three users. We use it regularly. 

You need three resources to handle the deployment. 

The tool is not expensive. Its licensing costs are yearly. 

I rate Elastic Search an eight out of ten. You can use the product if you are looking for value for money. 

We use the product for log analytics and metrics features. 

We can easily collect all the data and view historical trends using the product. We can view the applications and identify the issues effectively.

They could improve some of the platform's infrastructure management capabilities. There should be better visualization and insights about the cost of the SaaS services, which are not effective. Additionally, there needs to be more native integrations to merge the data.

We have been using Elastic Search for about a year.

I rate the stability a ten out of ten.

It is a highly scalable application. We have 15 users in our management team. I rate the scalability an eight out of ten.

I have experience working with Splunk in the past.

The initial setup for the SaaS platform is quite easy. We took assistance from an engineer for the onboarding. Thus, it was straightforward for us. However, there could be a better integration with AWS.

I rate the process a seven out of ten.

I rate Elastic Search's pricing an eight out of ten.

By integrating Deepgram insights with the product, we've gained visibility into logging, service behavior, and cost optimization.

I rate Elastic Search a nine out of ten.

We use the solution for search engines and indexing. 

The solution is valuable for log analytics. 

The solution's integration and configuration are not easy. Not many people know exactly what to do.  

I have been working with the product for five years. 

The product's deployment took a couple of days to complete. 

The product's deployment was done in-house by myself. 

I would rate the product a nine out of ten. 

We are internal integrators. We are in the bigger group as of now, but other groups, our clients, are affiliates from our group. They are our internal clients. 

The solution is currently on-premises.

I was mostly responsible for the SOC team, and I helped them create the detection rules for the production. I wanted to know how it could be implemented in different kinds of products, like Sentinel.

The most valuable features are the detection and correlation features.

Something that could be improved is better integrations with Cortex and QRadar, for example. 

I have been using this solution for no more than one year. 

Not really, because I'm not the engineer and so most problems appear during the installations or maintenance and I'm not in developing infrastructure operations.

The price of Elasticsearch is fair. It is a more expensive solution, like QRadar. The price for Elasticsearch is not much more than other solutions we have.

I would say that Elasticsearch is better than all the other solutions. QRadar is getting better, but it is still behind Elasticsearch in my opinion.

I would rate this solution 8 out of 10.

I would recommend Elasticsearch if you don't have bigger budget limitations to use other enterprise solutions or if you want to avoid the vendor lock-in.

We are using ELK Elasticsearch in a database. We use both Logstash and Kibana. Kibana is used for monitoring where the data is coming from.

The solution has good security features. I have been happy with the dashboards and interface.

There are some features lacking in ELK Elasticsearch.

I have been using ELK Elasticsearch for approximately two years.

We had some stability issues where we could not access the application.

We have approximately five people in my organization using ELK Elasticsearch.

All the installations were directly set up on the local servers.

The solution is free.

Elasticsearch is open source.

I rate ELK Elasticsearch an eight out of ten.

We use ELK Elasticsearch for storing application data logs.

Elasticsearch includes a graphical user interface (GUI) called Kibana. The GUI features are extremely beneficial to us.

Elasticsearch includes mechanisms for ingesting data into the cluster. So it would be great if those mechanisms could be simplified.

Improving machine learning capabilities would be beneficial.

I have been working with ELK Elasticsearch for four years.

We are using the latest version.

We have no issues with the stability of ELK Elasticsearch, it's quite reliable.

ELK Elasticsearch is a scalable product

This solution is used by five to ten people in our organization.

ELK Elasticsearch is used on a daily basis.

We have not contacted technical support.

We had a couple of issues that we were able to resolve by looking up the public information that is available on the internet.

There is a lot of community support for this solution.

The initial setup was straightforward and quite simple.

The installation took between six and eight hours to complete.

There is no maintenance required other than regular updates.

We completed the implementation internally.

Although the ELK Elasticsearch software is open-source, we buy the hardware.

The distributed installation is the way to go.

I would rate ELK Elasticsearch a nine out of ten.