Elastic Search Reviews

What we use this ELK (Elasticsearch, Logstash, and Kibana) solution is mostly for keeping firewall logs and collecting traffic flow information.

The scalability of this product is something that is very impressive and the performance is also very good.

I think the GUI part of the solution has the most room for improvement. Actually, we are using the free version. We do not use the plug-ins so we have to do some additional development ourselves to have the necessary access to the controls.

We are not a heavy user, we just keep the logs and track data in the system. We use it and there is no problem for our current purposes and level of use.

We have been working with the solution for just over a year.

Up until this point, there have been a few times that we did have some issues and we did not know what went wrong. But we have a guy who is dedicated to managing the system now and it is running pretty well. At this point, we do not have to spend much time in administration and maintenance paying a lot of attention to it. I would say it is pretty stable, overall.

We have around five people involved in using the solution.

The scalability is very impressive. We can do a lot of things with the product and have not explored all the possibilities as it is something we use somewhat lightly compared to its potential.

We do not yet currently use a full technical support plan. We are not really using the product extensively enough to warrant that expenditure. Up until now, our use has been light and the product is not heavily burdened. It has been performing as expected. When we upscale usage we will probably engage with a paid support plan.

The initial setup is not that problematic. It is obviously manageable as we are doing it by ourselves, so it is okay and fairly straightforward. We didn't need any assistance from integrators or consultants for the deployment.

Before choosing to go in this direction, we actually checked with some of the database options like the JSON option and Mango. The Elasticsearch product was referred to us by a friend at another company as a better solution for our particular need. They are using the system. After some tests and reviews of the products, we thought it would fit our needs, so we decided to go with it.

The advice I would give to others considering this solution is that you have to have someone knowledgeable managing the system. You have to know the needs, know how to manage queries, and understand the visualization. You have to have someone working on it and dedicated to it so that you can manage it. It is not just plug-and-play. If you decide to run with it, the performance and the result can be very satisfactory. We did not have any issues with achieving what we tried to do. When we need certain data, we always find it.

On a scale from one to ten where one is the worst and ten is the best, I would rate ELK Elasticsearch as an eight out of ten. What would make it a ten for us is something I wouldn't know at this point. Until we use it more heavily in production then we'll see how it performs under a full load and we'll have a better idea of what needs to be improved.

I'm a data scientist and we're a customer of ELK. We use the solution for multiple projects, mainly based around customer analytics.

I value the feature that allows me to share dashboards with different people with different levels of access. They can perform their own queries, like adjusting the time filter or hitting some other filters. It's very useful.

In terms of product improvement, ratio aggregation is not supported in this solution. I can do aggregations, but taking a ratio of two metrics is not supported. That's a common use case that I have come across. And if I want to do bulk coding then that's something that is not very convenient. I would like those things to be included in the next version. 

I've been using this solution for three years. 

Occasionally when you're handling large data you get some out of memory exceptions from time to time. It happens when you're doing pairing. Sometimes a few shots will fail. These are all typically when you're doing things on cloud on a large scale they tend happen.

It's a scalable solution. For now, we have about 10 users and we plan to increase that number. We use it regularly. 

I haven't needed to contact technical support. The forums are pretty good and most of the things that I need to ask are already answered so no need for support. The documentation and forums were enough. 

I haven't used other solutions. 

The initial setup is straightforward, it took about an hour. I did the setup myself. Some others also did it themselves and we had developers who put it up in the cloud for the others. 

We are currently using the Open Source version, so we didn't need to offset any licensing. For now, it's just the cost of maintaining the server. 

Our tech team did the research and I don't know if there were other options considered. 

You can test the product for your use case on their user free trial, they offer a seven or 14-day free trial, You can put it up on cloud and just push your data to check if your use cases are being handled or not. It's a quick test of the waters. 

I would rate this product an eight out of 10. 

I use Elasticsearch with Logstash and Kibana.

The most valuable features are the data store and the X-pack extension.

The user interface is ok.

The integration of Elasticsearch, Logstash, and Kibana is very good.

The pricing of this product needs to be more clear because I cannot understand it when I review the website.

This solution is scalable.

I rely on the community for technical support.

The initial setup of this solution is a little bit hard. I did not find it hard, myself, but it was difficult for my colleague who had less experience.

The deployment takes between one and two hours.

We implemented this solution ourselves.

The pricing of this solution is not clear.

This solution is ok for me and my business.

I would rate this solution an eight out of ten.

We use this solution to collect log data and analyze it. We have an on-premises deployment.

The special text processing features in this solution are very important for me.

As a system, it is easy to use.

This is not a robust system, so in terms of resilience, they have to make some improvements. From time to time the system goes down and we have to start again, after adjusting some configuration parameters.

Technical support can be improved.

The interface would be improved with the inclusion of dashboards to assist in analyzing problems because it is very difficult. Better dashboards or a better configuration system would be very good.

This is not exactly a stable solution, which is why we are considering another compatible tool, and whether we go on with Elasticsearch or change it.

I follow their forum and blogs, and I have also asked questions directly to their technical department. I would say that support is moderate. It is not very good or very bad, but in between.

We did not use another solution prior to this one.

The initial setup of this solution is easy and straightforward.

The deployment is both easy and quick.

We have an in-house team that handles deployment.

Two people are enough for deployment and maintenance.

We did not evaluate other options before choosing this solution, but due to issues with stability, I'm now trying out PostgreSQL for comparison.

My advice for anybody considering this solution is that it is an easy to use tool, but for work that is not complex. If on the other hand, the work is more complex, with more data and perhaps a clustering environment, then they may have to consider something more stable and more robust.

I would rate this solution a seven out of ten.

Our primary use case for this solution is to operate an integration platform for a warehouse management system.

This has improved our organization because we articulated Kubernetes, Docker, and GitHub with amazing simplicity in the scaling up of our service.

The most valuable feature for us is the analytics that we can configure and view using Kibana.

This product could be improved with additional security, and the addition of support for machine learning devices.

In my project, Elasticsearch is used to query terms for search and to provide data boards for our project team.

We have some important IT systems which provide support for our business processes. Money is a big issue. If something happens to the IT systems, we lose some money. 

We built Elasticsearch solutions to help us to search more quickly. After that, we can calculate how much money we can save by implementing Elasticsearch as a solution.

We had many reasons to implement Elasticsearch for search term solutions. Elasticsearch products provide enterprise landscape support for different areas of the company.

Elasticsearch is useful for different business processes, but there are some problems. We discuss these problems with the vendor and with our in-house team. We see the need for some improvements with Elasticsearch. 

We would like the Elasticsearch package to include training lessons for our staff.

Elasticsearch is stable. We have a normal relationship with the vendor and we have enough support.

Elasticsearch is very scalable. This was one of the most important points of our decision. We use Elasticsearch with about 1000 users. Now have two sets of solutions:

• One set works with the dashboard from the project team's site.
• A second set works with the enterprise department involved with two roles. 

As for the staff, we have an infrastructure team dedicated to Search. I can't answer quickly how many people we use for that. For maintenance, we employ about three to five experts.

We are planning to increase our Elasticsearch usage. We have the plan because some Elasticsearch systems integrate with other products and we must grow.

If we discuss technical support from the vendor, it's the first line of support and we don't need very quick response times in the first support line. 

If we discuss the second line of support, it must be conducted very quickly. About technical support from the vendor, it's not fast. It's normal.

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

We did not have difficult trouble installing and configuring Elasticsearch. The setup is straightforward.

The implementation depends on what part of the solution. In some areas and for some Elasticsearch installation tasks, we wanted third-party consultation support.

We don't integrate Elasticsearch on enough systems to complete the estimate of ROI.

For the next project, we might buy the license, but we don't use it now. I don't know exactly what the license fee for Elasticsearch is currently.

This is a difficult question because we had a specific reason for choosing Elasticsearch. Different solutions provide different benefits. We compare these and choose one solution over another. 

Overall, it depends on the manufacturers. We compared Elasticsearch with other products like Riverbed, for example.

In general, we have many advantages from the features of Elasticsearch, but there are questions mostly about money, not about technical features, that are of concern.

I would rate Elasticsearch eight/nine out of ten. We have enough possibilities and features with Elasticsearch for our business requirements.

Various purposes, mainly log analysis.

This product has notably improved the way we store and use logs, from having a more user-friendly, centralized solution (for those who just needed a quick glance, without being masters of sed and awk) to implementing various mechanisms for machine-learning from our logs, and sending alerts for anomalies.

The three major features which won us over to Elasticsearch were:

• The well written documentation
• The already available integrations with multiple other tools related to our needs (like Logstash, Kibana)
• The easy with which scalability was achieved.

There are some areas in which Elasticsearch could improve: 

By honoring Unix environmental variables and not relying only on those provided by Java (e.g. installing plugins over the Unix http proxy). 

Performance improvement could come from skipping background refresh on search idle shards (which is already being addressed in the upcoming seventh version).

Until now, we have not run into any issues running Elasticsearch that were not based on bad capacity planning based by us.

Elasticsearch is a very easy to scale product, compared to other similar technologies.

To date, we haven't had the chance to use Elasticsearch's technical support.

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

Our initial setup was very easy to do.

We evaluated HBase and Cassadnra.

ELK has helped my team leverage a powerful and efficient capability that is comparable to more costly solutions.

The ability to aggregate log and machine data into a searchable index reduces time to identify and isolate issues for an application. Saves time in triage and incident response by eliminating manual steps to access and parse logs on separate systems, within large infrastructure footprints.

Enterprise scaling of what have been essentially separate, free open source software (FOSS) products has been a challenge, but the folks at Elastic have published new add-ons (X-Pack and ECE) to help large companies grow ELK to required scales.

No issues with stability.

We encountered issues with scalability.

Not applicable, for my team's experience with ELK. Being a FOSS, there is limited support from Elastic without a service – support, consulting, training. There is wealth of information on the web and a growing community of users to lean on for support, though.

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.

Initial setups were complex years ago, but they are more straightforward in the current offering. ELK is essentially a collection of products that each requires infrastructure and expertise to set up independently, and connecting them to gain a functional tool requires still more expertise.

This is a free, open source software (FOSS) tool, which means no cost on the front-end. There are no free lunches in this world though. Technical skill to implement and support are costly on the back-end with ELK, whether you train/hire internally or go for premium services from Elastic.

Splunk, Sumo Logic, and IBM’s Operation Analytics.

Try it out. There is little to lose but time.