Elastic Search Reviews

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.

Our primary use case of this solution is for monitoring our logs and infrastructure. We are customers of ELK and I'm a system administrator. 

A positive feature of ELK is that it directly interacts with Elasticsearch. The UI is very nice, and performance wise it's quite good too. A key feature is that this is a reasonably priced monitoring solution.

We run this solution on multiple servers. ELK has three lanes which comprise a single package made up of Elasticsearch, Logstash, and Kibana. To my mind, this is not efficient because we have to individually deploy the different applications. In contrast, we're able to deploy Splunk with a singe application. Implementing the dashboards is also quite difficult. With Splunk and Nagios it's much easier to directly interact with Elasticsearch. I'd like to see some additional features in the front end which currently make it a bit difficult to implement and it should be simplified.

I've been using this solution for six months. 

This solution is stable. 

This is a scalable solution, we have eight to 10 users. We had initially planned to expand use of ELK because of its cheap price and the services that are included, but given the difficulty with implementation we've decided to go with Nagios instead. 

The technical support people are very knowledgeable but the response time is quite slow which is not very good. 

The initial setup of ELK is more difficult than the setup of other monitoring applications. I was able to carry out the deployment alone. 

For anyone looking to implement a monitoring product with almost no cost or at a cheaper price, I would suggest the ELK stack. However, it does require a high skill set because of the difficulty with implementation. 

I would rate this solution a six out of 10. 

Elastic Search is added advantage for us because we normally use it for our uptime monitoring and our log analysis. When we merge it with Splunk, it helps us correlate and do security monitoring. 

Elastic Enterprise Search comes embedded within a solution that we have developed for our clients. It's a payment solution. We've recently shipped it with Elastic Enterprise Search embedded. All the logs and all the internal communications get captured by Elastic Enterprise Search. It makes it easy for the IT teams who are doing uptime monitoring and troubleshooting to have a look at it. We have the security teams develop their own monitoring metrics and logs, if they wish, based on their deployment. 

The beauty of Elastic Enterprise Search is if they also have their own third-party tools, there's the ability to integrate and read off Elastic Enterprise Search and have any third-party tool process the logs as well. It is highly extensible.

The most valuable features of Elastic Enterprise Search are it's cloud-ready and we do a lot of infrastructure as code. By using ELK, we're able to deploy the solution as part of our ISC deployment. 

The extensibility and configurability of the solution are great. Having the ability to mine for anything is useful. It's extensible and useful in terms of digesting any type of information. Since we do a lot of consulting, it means we are able to apply it to diverse environments without having to suffer the overhead of integration.

There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone.

I have been using Elastic Enterprise Search for approximately four years.

I have no complaints in terms of stability. However, you have to make sure you give Elastic Enterprise Search the minimum resources it requires. We have not seen any major issues that we would send back to the vendor or the solution maker. If there was an issue it most likely would be from the environment, depending on how it was deployed and how it was configured.

Elastic Enterprise Search is scalable. In our environment, we deploy it in a containerized environment. For us, we've experienced the scalability of the solution because as we grow and expand, we spin up more containers that are interconnected. I don't see any issues with Elastic Enterprise Search from a scalability perspective. 

There's a lot of material available online. We tend to look online before we reach out for technical support. We have not needed to contact the support and this is a testament to how much information is available online. 

The solution is not expensive because users have the option of choosing the managed or the subscription model. 

Elastic Enterprise Search is a very good solution and they should keep doing good work.

I'm a very satisfied customer because almost everything I need comes out of the book. You already have machine learning, alerts, the ability to search, APIs, inbuilt security, and integration to third-party authentication.

I rate Elastic Enterprise Search a ten out of ten.

Elasticsearch is one of the NoSQL databases available. My application is a microservices application where the data gets published on a Kafka cube. It allows us to connect to Kafka and get this data in a document format very easily. I'm using Elasticsearch as my backend processing database, where I'm building and reporting using Kibana.

I like how it allows us to connect to Kafka and get this data in a document format very easily. Elasticsearch is very fast when you do text-based searches of documents. That area is very good, and the search is very good.

The price could be better. Kibana has some limitations in terms of the tablet to view event logs. I also have a high volume of data. On the initialization part, if you chose Kibana, you'll have some limitations. Kibana was primarily proposed as a log data reviewer to build applications to the viewer log data using Kibana. Then it became a virtualization tool, but it still has limitations from a developer's point of view.

I have been using ELK Elasticsearch over the last two years.

The price could be better.

I would tell potential users that they have to locate the data source and understand the data. They will have to decide on whether they have to go for a NoSQL or a relational database. 

If it's NoSQL, then what kind of data are you seeing? If it's more textual data, then you're going to read more. So, I would recommend Elasticsearch. Otherwise, you have other databases like MongoDB and Cassandra.

On a scale from one to ten, I would give ELK Elasticsearch a seven.

ELK provides 

• good end to end log monitoring
• Logstash grok pattern
• elasticserach indexing
• easily configurable Kibana dashboards
• feature rich and appealing Kibana UI, as its USP.

The product offers a powerful, cost effective solution for proprietary log management and is easy to understand and start with. This saves cost as well as enhances usability.

The open source version should ship basic security versions with it. Alerting is an important feature which is not available in the open source stack.

About two years.

Stability issues can be seen with Logstash and can be addressed using Kafka. Also, data collections may not be 100% optimal using Filebeat and could be further improved.

The product is highly scalable, especially on the database side (elasticserach).

Have worked on the open source version and, hence, I don't have much information about tech support.

I have not worked with another solution.

The setup is straightforward for PoC, but becomes complex while scaling it or deploying it for production.

Yes, I evaluated GrayLog and Fluentd, but ELK was more feature rich.

The ELK open source stack is rich in features but lacks the essential security features.

ELK is one of the best open source alternatives for log management. If alerting and security features can be built by the team internally, on top of the stack, then the product is for sure going to add a lot of value to business.

I am using it to get some hands-on experience and learn the product by searching, building use cases, test cases, dashboards, and visualizations.

With hands-on experience, you learn more about the product and how it works.

It should be easier to use. It has been getting better because many functions are pre-defined, but it still needs improvement.

If you have a large enterprise environment, it is costing a lot of money and it's not a full-blown SIEM. It has SIEM features but a lot is missing. You need to involve other products to make a SIEM out of it.

Some of the other products needed were Apache, Kafka, and ticket tools. It was custom made and not what I had expected in the end.

I would like to see them get closer to a full-blown orchestrated SIEM, and create predefined modules to bring you to using it as a SIEM faster, and on the fly instead of having to tweak the Grok filter for weeks.

I would like to see more pre-defined modules.

I have been using Elasticsearch for two weeks.

We are not using the latest version, but not an old version.

It's a stable solution and we have not had any issues.

The scalability is fine.

I have contacted technical support, once or twice. The experience was okay.

The initial setup was okay, not as easy as Splunk but it was manageable.

The pricing model is questionable and needs to be addressed because when you would like to have the security they charge per machine. If you are building any cluster and you are paying €6,000 per machine, that is expensive.

I think that Elasticsearch is a good product and cheaper than Splunk.

I like this solution, but it has too much hands-on time required tweaking to get it up and running.

I have no plans to continue using this product. Currently, I am focused on SIEMonster because I signed a partnership and I would like to sell a total product. It doesn't make sense to spread across multiple products. 

I would like to earn money out of it, so I'm focusing currently on SIEMonster.

I think that Elasticsearch is a good product and cheaper than Splunk.

When I check Gartner, I don't see mention of Elasticsearch, it seems they need to make some improvements.

I would rate this solution a seven out of ten.

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. 

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.