Elastic Search Reviews

The solution is a dashboarding tool that's useful for DevOps engineers for monitoring. The solution is like a log database. You can ingest into it anything you want and then find the value of the things you ingest. The solution can also be used to make reports.

The most valuable feature of the solution is its utility and usefulness. I use the solution to see the logs better or the error explained. The solution allows us to be more on top of the alerts for the logs. The solution makes passing of the logs easier and faster.

I would like to see more integration for the solution with different platforms. Sometimes, it's hard to understand what you need to send to Elastic Search.

I have been using the solution for two to three years.

Elastic Search is a stable solution.

More than 50 users are using the solution in our organization.

We use the solution's live data analysis for operations purposes. The solution also has a monitoring aspect. ElasticSearch is like a middleman between the PRTG and ITSM tools. It is easier to pass the information about the metrics or the full logs of the cloud platform you are ingesting in the solution instead of giving the output to PRTG.

The solution is deployed on the cloud in our organization. Elastic Search is something that comes after the projects are done. After implementing the project, we use the solution to have that project monitored. I would recommend the solution to other users.

Overall, I rate the solution an eight out of ten.

We have data in different databases. One is a relational database, and another is NoSQL. They are different services. They host document-like data. We used Elastic to convert the data structurally. We used Elastic as a multi-service search engine. It is a good solution. It is too powerful.

I would advise anyone to use the product. It is good. Data indexing of historical data is the most beneficial feature of the product.

The solution must provide AI integrations. I could direct my data flow to my AI tools if I use Elastic for IoT data.

I have been using the solution since 2007.

I rate the stability an eight out of ten.

The solution provides powerful scalability. I rate the scalability a ten out of ten. Our clients are medium-sized businesses.

I do not need technical support because the product works well.

The initial setup was very easy. I rate the ease of setup an eight out of ten. The setup can be done within minutes.

I use the community version. The premium license is expensive. I rate the tool’s pricing an eight out of ten.

With the power of Kibana, we can easily and dynamically analyze and summarize our log data. The internet has information about all the technical solutions. I bought some courses from Udemy for Elastic Search. I also got some documents from Elastic Search. The documentation for Java is very good. It was sufficient to learn as a developer.

I could integrate my products to Elastic Search easily. I use the default index for my solution, and it works very well. Elastic’s indexing policies are very good. I do not need any indexed operations for my solution. Overall, I rate the tool a nine out of ten.

The primary use case of this solution is to search large amounts of data across multiple systems.

The solution has improved our organization by allowing us to quickly search data from multiple systems saving valuable time.

The most valuable features are full-text search, the ability to index large amounts of data, map data in areas that are not fully structured, and scaling out.

The one area that can use improvement is the automapping of fields.

This may have been improved in the latest version.

I have been using the solution for a year.

The solution is stable. 

The solution is easily scalable.

There has not been a need to use customer service or support because of the vast amount of reliable forums available online.

The initial setup is straightforward. If you understand Linux you can deploy in a couple of days.

The implementation was completed in-house.

To access all the features available you require both the open source license and the production license.

I rate the solution seven out of ten.

In cases where the memory of the nodes is exceeded, you will need to manually step in to delete some data, otherwise, the solution maintains itself automatically with little need for human intervention.

The forced merge and forced resonate features reduce the data size, increasing reliability.

The open source license is not enough when dealing with a large amount of data. The production license is required when you have larger requirements.

I recommend the solution to anyone who needs to integrate a lot of old systems into a data lake.

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.

We are mainly using it for analytics reports for the data taken from our call center. We are using the entire stack. We are using Kibana and Elasticsearch. Kibana is the front end for dashboards, reports, etc.  

The flexibility and the support for diverse languages that it provides for searching the database are most valuable. We can use different languages to query the database. 

It is hard to learn and understand because it is a very big platform. This is the main reason why we still have nothing in production. We have to learn some things before we get there.

I have reported and had discussions about several bugs at discuss.elastic.co, but that happens with many products. It is not only with this product.

We have been using it for about one year, but it is not yet in our production environment.

It is reliable.

If you use a cloud platform or a cloud environment, it is easy to scale. 

For on-premises, we are using OpenShift. We are using a cluster on OpenShift, and we are facing some issues, but they are not related to Elastic. They are related to our infrastructure of OpenShift because OpenShift is deployed on VMware, and the storage of VMware doesn't allow us to take backup snapshots in a secure way. We are thinking of migrating this cluster of OpenShift to another platform.

Currently, we have a few users of this product because we have been using it only for one year, and we are the first ones in our company. In the future, we will have more people involved with the product.

We have only used their community support from the discuss.elastic.co site.

There is a free version, and there is also a hosted version for which you have to pay.

We're currently using the free version. If things go well, we might go for the paid version.

It is a good choice, but you have to take your time to learn it. Its learning curve can be hard. 

I would rate it an eight out of 10.

While the solution is slated for making logging positions more centralized, at present we are gearing through it. A fully-fledged deployment of alignments is not yet in place.

We have adjusted the logs into the spec for a couple of our applications.

We consider all of the features to be valuable. With respect to 12B Kibana, all of the components fit in very well. Logsearch gives us certain log filtering capabilities and we can vet what we push into our database. This allows us only to log and ship limited items. Essentially, Logsearch plays a big role although not the most important one. 

The solution itself needs improvement. There is an index issue in which the data starts to crash as it increases.

This leads to an impact on the solution's stability.

The index and part of the solution's stage have weak points.

In the next release, I would like to see better plugins when integrating with, say, Microsoft Teams.

The Kibana dashboard is quite user-friendly and we have had no issues involving our technical team. However, some technical knowledge is required, especially if one wishes to create dashboards and as it relates to index management.

I have been Vusing ELK Elasticsearch for plus or minus two years.

ELK Elasticsearch is definitely a stable solution. It is the spec that surprises most of the other logging solutions in the market.

The solution is quite scalable and this is one of its advantages. We are trying to add or plug on to Elasticsearch at present.

We have been open to solutions and haven't really had a need to rely on technical support. We've relied mostly on support forums.

This said, I would rate the support well, as we initially interacted with the support team and made use of Google.

The initial setup had a bit of a learning curve for us while we acclimated ourselves to the use of the solution. However, after a while, it became quite easy. 

I would not say there was much complexity even at the outset, as we have an understanding of how to troubleshoot and do the installation.

There is more than enough documentation of the solution online. It is useful and you can find what you're looking for. There are also forums that can be of assistance. 

While I cannot say for sure, as our organization is structured so that we work in silos with everyone looking after his own infrastructure, I would estimate that we have approximately 200 employees making use of the solution.

My advice to others who are considering implementing the solution is that they first make a plan to figure out how they wish to cluster the solution and the amount of data that must be ingested. Much planning would be involved. It would be wise to start with the open-source solution, which comes with many advantages, and to move on to the Enterprise version if there should be a need for dedicated support. 

I cannot posit whether management will wish to take this route, although this is definitely worth considering, as we are talking about a fully robust infinite solution across the board. 

I rate ELK Elasticsearch an eight out of ten.

We are primarily using it for monitoring. It is used for server monitoring.

I really like the visualization that you can do within it. That's really handy. Product-wise, it is a very good and stable product.

They should improve its documentation. Their official documentation is not very informative. They can also improve their technical support. They don't help you much with the customized stuff.

They also need to add more visuals. Currently, they have line charts, bar charts, and things like that, and they can add more types of visuals. 

They should also improve the alerts. They are not very simple to use and are a bit complex. They could add more options to the alerting system.

I have been using this solution for one year.

Stability-wise, it is very good. Once the data starts coming in, it is very stable. I didn't find any big glitches in it.

We contacted their technical support once. I didn't find them very good. They are there just to provide documentation and stuff. They don't help you much with the customized stuff. They could improve that. I would rate them a two out of five.

It is complex because it is not Windows-based. It is Linux-based, so one must know Linux to deploy it properly. It is not a product that you can install with just multiple clicks. You need to understand it.

It seems good in terms of return on investment. It is a monitoring solution, and it triggers alerts before something happens. For example, it triggers an alert when the space in Windows reaches an 80% limit. I would say it is a good investment. We are able to fix things before they go wrong. If we didn't have Elasticsearch, things would go wrong, and we would be spending more time fixing them later on.

I would advise others to first know Linux because it would most probably be on Linux. If you're good at Linux, you will be good at this as well.

I would rate ELK Elasticsearch an eight out of ten.

We are developing a SIEM application that is similar to QRadar, ArcSight, or Splunk. This application uses Elasticsearch as its search engine because we want to retrieve information fast. We are just using the basic search engine part of Elasticsearch. We have developed lots of things on top of Elasticsearch, such as security, correlation, reporting, etc.

The search speed is most valuable and important.

Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. 

Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one."

In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. 

Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release.

I have been using this solution since version 1.0.

For a one-node installation, it is easy. You can do it and retrieve information fast, but when you are trying to scale up, everything becomes complicated. If you want to deal with several terabytes of data, you should read whitepapers or case studies or get proper consultancy from Elasticsearch. Otherwise, you will lose data. I know many customers who lost their data and could not recover it. It is not like you store everything and search for everything, and it is just instant. It is not like that. You should do your homework very intensively. It looks easy, but when you scale up, it gets complicated.

We got 60 days of development consultancy with them. Until we sign the agreement, they were quick and prompt. After the signature it changed. Overall experience, we are not satisfied with the development consultancy.

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.

Its initial setup is straightforward. There were no problems.

We are using the Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition.

We evaluated other products and chose Elasticsearch because the data that we are collecting is unstructured. Every log has a different structure.

The most important thing to keep in mind is that it is not as they advertise on their site. If you want to scale up and are looking for a big deployment, you must read everything. You also need support from the company itself. 

I would rate ELK Elasticsearch a seven out of ten.