Elastic Search Reviews

We use Elastic Enterprise Search to develop robust and competitive projects. 

We provide clients with a chance to upload a file. They have all the accounts and transactions that they want to do and we have a limit of 10,000. In two minutes, they can have the 10,000 transactions in their accounts.

I would like to see Elastic Enterprise Search focus on interbank transfers, maybe in another way of payments. We have a feature focused on online interbank transfers. But, it would be good to see current payments for workers. They could also provide this same product to the retail clients. The price support could be improved as well. 

We have been using Elastic Enterprise Search for about a year and a half.

Elastic Enterprise Search is stable. On a scale of one to 10, with one being not very stable and 10 being very stable, I give Elastic Enterprise Search a nine. 

Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10. 

Elastic Enterprise Search's tech support is good but it could be improved. 

Positive

The initial setup is difficult. On a scale of one to 10, with one being very difficult and 10 being very easy, I would rate Elastic Enterprise Search a two. 

We are continuously integrating new features. Everything we added this month took about a day each to deploy. We have 22 software engineers on the project, along with two tech leads and one solution architect.

The price we pay for Elastic Enterprise Search is very high. We have a complicated banking project with a lot of components, developers, and features. 

On a scale of one to 10, with one being very cheap and 10 being very expensive, I would rate this solution an nine. Their pricing system is highly complex. 

Our company uses the solution for centralized logging and monitoring. We have slowly moved our Stackdriver to the solution as a cost-cutting measure. 

We have more than 100 technicians using the solution. 

The observability is the best available because it provides granular insights that identify reasons for defects. The observability is more powerful than Grafana because it is so granular.

The UI point of view is not very powerful because it is dependent on Kibana. This can be a struggle because it is not clear where observability features such as logging originate. The UI visualization could be more interesting.

For example, a centralized login for a strike driver only provides two choices for viewing. You can either view the log for an individual system or view the log at the centralized level. A more granular approach with locations, pods, and servers is preferred. 

For comparison, Stackdriver is awesome because it includes all information with respect to the UI point of view. 

I have been using the solution for a few months. 

We are still exploring the solution but find it to be very stable at the enterprise level. It is not a new product, its stability is trusted, and it is well suited for enterprise applications. Extra features are released with no stability issues. 

The solution is definitely scalable and that is one of the reasons we moved from Grafana. We use Spring Boot but the Spring Actuator's micrometer does not scale properly and is very slow. The solution can scale and manage all our monitoring needs in one place. 

Our team is able to solve issues so we do not need technical support. 

I previously used Stackdriver. 

The initial setup is difficult because the solution is an independent product that requires integration with the running system. A one-time configuration is needed for both cloud and on-premises systems. This is common for independent products so is not a big deal for our company. 

For comparison, Stackdriver is already built in the GCP so there is minimal configuration when deploying services in the GCP environment. 

We implemented the solution in-house. 

The solution is less expensive than Stackdriver and Grafana. 

Our company has a relationship with Google so we explored Stackdriver. Its monitoring and logging capabilities are interesting but observability is not that good and it is a bit costly. 

We slowly moved our logging dependencies from Stackdriver. Sometimes we used Splunk but we also used the solution and Grafana because our product is a bit dependent on Spring Boot. 

We found that the solution is more powerful than Grafana with respect to observability and it is more cost effective. 

When using the solution, it is important to understanding indexing concepts and the proper way to search logs from a visualization point of view. These two items work together internally to produce logs that can be filtered to specifications. 

I rate the solution an eight out of ten. 

Elastic Enterprise Search is the repository for time series and data from the onsite instrument that monitors variables in our mining infrastructure called tailing dams. We monitor the tailing dams' physical stability and take the information from the sales force and manual data introduced by the operators. The system captures the information in the Elastic Enterprise Searchtime series, and we make calculations and trigger events and alerts based on those calculations. We save them as well as the events and alert times.

Elastic Enterprise Search is a nonstructured database that can manage large amounts of nonstructured data. We also use a structured SQL database. I am unsure why our technical people selected Elastic Enterprise Search. The people that started the project selected open-source software and recommended the ETC component required in the system architecture. The Elastic Enterprise Search has been defined from the beginning of the project and fulfills the project's requirements. However, there is a lack of technical people to develop, implement and optimize equipment operation and web queries. This may be a problem with the provider, and they currently lack the resource to optimize the performance of the database.

Finding skilled people to work with Elastic Enterprise Search in the project team has been difficult. This may be because the development team has not considered it. It is important to improve the database performance because there is a large amount of data and the optimization of the queries and the system's performance are very important.

We also use three other databases, MinIO, PostgreSQL and PostgreSQL. We have a very skilled person on our team that knows how to use all these products. However, he's not responsible for optimization because it's the responsibility of the Indian provider that has to develop the application.

It is fairly stable.

It is a scalable solution. 70 people are working with this solution in the project, 35 on the development team and 20 backend people. We are working on the development, but it's part of the service that the Indian company has to provide. There are about 50 people on their development team who deal with all the development, infrastructure implementation, architecture definition and implementation of the software stack. We are the counterpart of that company.

Since it is open-source, we don't pay licensing fees. In the development and QA environment, we don't pay anything. We, however, have to pay for all the software, subscription, pre-protection and protection.

I rate this solution a seven out of ten. Because it is open-source, there is no technical support provided by the vendor, so we are moving to enterprise subscriptions for each of these products. We are allowed free licenses and implement enterprise or commercial licenses and the production of protections.

An original criterion selects the software stack because they have to be good tools, but they all have to be open-source. Nobody considers it because the original team that started the project worked in an investigation organization and was closer to open-source software.

They are not clear regarding the support of their solution when they go into production. That's why we are updating the licenses to interpret license subscriptions and assume their support for each software component.

We are using Elastic Enterprise Search for monitoring and alerting. It will look for any kind of possible error that is on the infrastructure side and give notifications.

The most valuable feature of Elastic Enterprise Search is the opportunity to search behind and between different logs.

Elastic Enterprise Search can improve by adding some kind of search that can be used out of the box without too much struggle with configuration. With every kind of search engine, there is some kind of special function that you need to do. A simple out-of-the-box search would be useful.

In the next release, they could improve on the scheduling and alert features.

I have been using Elastic Enterprise Search for a couple of years.

Elastic Enterprise Search is stable.

Everything is managed by Amazon AWS, making Elastic Enterprise Search highly scalable.

We have approximately eight engineers using this solution in my company.

I have not contacted support.

The initial setup of Elastic Enterprise Search was straightforward.

We did the implementation of Elastic Enterprise Search in-house with one person and it was up and running within a couple of days. There is detailed documentation that helped us.

There is fine-tuning needed, but that's never-ending because every time you add a new server, features, or tools inside you have to tune it a little bit better for the alerts.

Elastic Enterprise Search is an open-source solution.

I rate Elastic Enterprise Search a ten out of ten.

We are using the solution for our products. We are keeping some DBs where we are doing pattern searches. On the application side, we are keeping those in Elastic and a huge amount of data for our different product lines.

The way we access it is great.

The scalability that Elastic is providing is quite useful. 

We can do a lot of archiving. 

It is stable. 

The technical support is quite good. 

The cost is too high once you deploy the solution. 

They're making changes in their architecture too frequently. We'd like less frequent updates. 

I've been using the solution for five or six years. 

The solution is quite stable. There are no bugs or glitches. It doesn't crash. It is reliable. 

It's a scalable solution. We can expand it if needed. We have 50 to 60 users on the solution right now. We do not have plans to increase usage at this time. 

We've dealt with technical support in the past and have had very positive experiences. We are satisfied with the level of support we get. 

Positive

The initial setup has a moderate amount of difficulty. It's not simple and not overly complex. 

Since we are paying more for the license, we have not seen a very high ROI. 

The developer and tester licenses are one thing that is not hurting us. However, the deployment license cost is very, very high for Elastic.

We did look at other options five or six years ago. We chose Elastic for multiple reasons in the end. 

I would recommend the solution to others. 

I'd rate the solution nine out of ten. 

The solution satisfies our business needs.

The most valuable feature of Elastic Enterprise Search is user behavior analysis.

Elastic Enterprise Search could improve the report templates.

I have been using Elastic Enterprise Search for a while.

Elastic Enterprise Search is stable.

The scalability of Elastic Enterprise Search is good.

I have not contacted the support from the vendor.

Elastic Enterprise Search is of a moderate range of difficulty, it is not difficult and not easy.

We are paying $1,500 a month to use the solution. If you want to have endpoint protection you need to pay more.

I rate the price of Elastic Enterprise Search a three out of five.

My advice to others is for them to make sure this solution satisfies their business needs because there are many solutions and providers, with a lot of options. There are solutions that have a lot of features that the business might not need and it is not good for the business to waste money on features not used. It was recommended by many peers not to seek many options in a solution that you are not going to use, and to concentrate on what is needed.

I rate Elastic Enterprise Search a seven out of ten.

I am using Elastic Enterprise Search for the visualization of logs.

The most valuable feature of Elastic Enterprise Search is the Discovery option for the visualization of logs on a GPU instead of on the server.

Elastic Enterprise Search could improve its SSL integration easier. We should not need to go to the back-end servers to do configuration, we should be able to do it on the GUI. 

I have been using Elastic Enterprise Search for two years.

Elastic Enterprise Search has been a stable solution for me for the whole time I have been using it.

I am using Elastic Enterprise Search on-premise and it cannot scale. However, they do have a cloud option.

We have approximately 100 people using this solution in my organization. We use it on a daily basis.

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

The setup of Elastic Enterprise Search is not normally easy but I was running it on top of Docker which made it easy.

I rate the initial setup of Elastic Enterprise Search a three out of five.

I have configured the solution myself and it has provided me with what I want. I do maintenance of the solution once every other week.

The version of Elastic Enterprise Search I am using is open source which is free. The pricing model should improve for the enterprise version because it is very expensive.

We chose Elastic Enterprise Search over other solutions because the interface was easy to use.

I rate Elastic Enterprise Search a nine out of ten.

All my use cases have been based more on observability for IT operations. We deal with it in terms of metrics, logs, transactions, traces, and so on. 

In terms of enterprise, most of the use cases are based on search capacity within the company to find documents and relevant information. That is the main use case.

The most relevant feature for me is the platform capacity. I consider the capacity high-performance with a distributed model that can support it, and recently we are growing. 

Search is really powerful. All the search engines and the rules that complement them allow the users to create different kinds of administration for the platform. YOu can create synonyms or rules to better understand or to better detect partial search criteria. It's like an AI that boosts searchability.  

The platform has a powerful tool to correlate and create rules that understand what people will be searching for. 

All the community support that we have available from different users in the open source community is great. Everyone shares and publishes all of these different use cases. That makes the platform and the platform understanding really powerful for anyone who wants to implement a different case.

It is easy to set up.

The solution scales well. 

They have great integrations on offer. 

Maybe Elastic Search could improve the analytics part of the search so it can be more powerful to the user. It could help provide more understanding of what people are searching for. 

We'd like more user-friendly integrations. It should be easier for non-technical people to understand how to handle them. 

I've used the solution for the last four years or so. 

It's stable. We have on-premise and on-cloud deployments. It's stable on both. I prefer the cloud as I avoid the time it takes to manage the platform. However, both cases are stable.

It is a product that can scale well. It's not a problem. 

We have maybe 200 people on the product right now. 

I have experience working with technical support. They are good at responding to incidents. I have not had too many incidents, however, sometimes for probably technical questions in terms of platform performance, search, cluster distribution, and so on, I might reach out. 

My point of view is that the technical support is awesome. They are very responsive and they have a really high understanding. The team has a lot of people with a lot of technical skills and technical knowledge.

The initial setup is very straightforward. It's not difficult as well. 

As I use the cloud, all of the costs for me are based on customer needs. There is a fascinating calculator published in Elastic. That there is not a specific starting cost. It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process. That said, you can start with a small budget, implement the use cases, and start growing slowly.

I'd rate the solution nine out of ten. 

I'm a customer and end-user.