Elastic Search Reviews

Elastic has a lot of products. The one I'm most familiar with is Elastic Observability. It's designed to monitor our applications within an organization. It gives managers visibility into the activity and functionality of applications within the network. I've worked with it both on-premises and in the cloud. It helps us monitor applications and identify any issues. For example, we can see if an application is calling on a database if there are any delays or errors, and what might be causing those problems. It can also give us a proper view of the number of transactions done on the database and other information. It's not just pulling data for us; it's giving us real-time insights into the activities and functionalities of our applications within our network environment.

When users understand the root cause of the problem, they spend less time resolving it. The number one benefit is end-to-end stability. It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all your cloud-based and on-prem applications. It also simplifies issue resolution, leading to faster resolution times and optimized performance. It is achieved through numerous tools, metrics, and application performance fine-tuning systems, ensuring a smooth user experience. That's why many enterprises seek this kind of solution. It provides valuable insights into potential security vulnerabilities, enabling pre-emptive measures and safeguards for your data assets. Then there's data-driven decision-making, which is very important! It breaks down data silos by ingesting all the telemetry data (metrics, logs, etc.) into a single, scalable platform with a contextual data model. This flexibility allows you to collect and visualize any data from any source. Essentially, it pulls data from all sources and guides you in making data-driven decisions for capacity planning, resource allocation, and risk mitigation. Finally, it also fosters collaboration across IT teams.

There are potential improvements based on our client feedback, like unifying the licensing cost structure, which might be helpful for clients. This room for improvement is from my perspective as a salesperson. Because when I give customers the pricing information, they might wonder why there are two different licensing models, unlike competitors like BeyondTrust or Delinea. Delinea also has the same thing with the code.

I have been with this solution for more than six months.

It's very, very stable. Most times, I go through the demo sites, which allows understanding of functionalities and use cases and all of that. I would rate the stability a nine out of ten.

It is a scalable solution. I would rate the scalability a nine out of ten.

The customer service and support are very nice.

Positive

I have experience with Delinea, ManageEngine, BeyondTrust, IBM and WALLIX. But compared to Elastic, they lack the same level of artificial intelligence capabilities. It's like an all-encompassing package with tons of features. One of those features is the ability to pinpoint the root cause of any problem, whether it's code issues (like it was not written properly), developer errors, or anything else. It goes beyond just surface-level troubleshooting and digs deep to give you the real why. That's what sets it apart from the others. Imagine an application is having some issues. Elastic can tell if it's faulty code, a developer mistake, or anything else. It gives you the true root cause, not just the surface-level symptoms. That's its strength and why it stands out as the industry standard.

The initial setup is not complex to me. I've seen it displayed before in a demo presentation with Jakadaz. The solution is not difficult to use. It's very easy. Even as a non-technical person, I could interact with the application.

The deployment doesn't take long because we have experts who can help. It's available both in the cloud and on-premises, so it depends on the customer's choice.

It is a cost-effective solution. It is not expensive.

I would rate it a nine out of ten for now. It has a lot of features compared to other solutions. Its comprehensiveness and range of features are what make it stand out for application monitoring. I highly recommend it. It's very good because it's efficient, highly scalable, and has high availability. Additionally, cost-effectiveness is crucial in Nigeria due to exchange rates. Organizations need solutions that are affordable, and Elasticsearch fits the bill. I would absolutely recommend it to any organization.

Hybrid Cloud

We have a distributed login environment. We have logs in databases and some in files. We use the solution to centralize everything. It's good for monitoring. 

The solution is useful for observing logs. The observability is good. 

It's good for collecting various types of logs. The metrics on offer are great.

We also collect logs from VMs, and we can look at the CPU and RAM situation to see what is being used. 

The APM for our ITSM tools is helpful. It provides good visibility.

It is scalable.

The solution offers good stability.

The initial setup is easy.

We'd like to see more integration in the future, especially around service desks or other ITSM tools. 

I've been using the solution for two or three years. 

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

It is scalable. It's not a problem if you need to expand it. 

We have about 20 people using the solution right now. We're using it in a test environment right now. Once we deploy to production, 300 to 400 people will use it. 

We have never used technical support. 

Our help desk also uses Grafana. We'll use this solution more widely eventually. 

The initial setup is very easy.

We took about a month to deploy the solution. 

We might need about ten people to handle the deployment and maintenance. We're still in the test environment right now. 

We handled the setup ourselves. We did not need outside assistance. 

I'm not sure of the exact licensing costs. I don't deal with that aspect of the solution. 

I'm using the latest version of the solution. I started with version 7.1, and now I use 8.6.

I'd recommend the solution to other users. 

I'd rate the solution nine out of ten. The features and tools are overall very good. 

On-premises

We use the solution mainly for logs today. There are other teams that use it for other use cases. We just use it for logging and logging search and these kinds of things.

The search capabilities are the best that we could find. It's great for searching for any text with wild cards inside the logs. It's very good. We have a very good performance, even with billions of registries.

The solution is stable and reliable. 

We have an issue with the volume of data that we can handle. When we have a lot of data, like 30 days of logs, the product becomes slow, and we had to reduce it to seven days. Now, we have only seven days of logging.

Logging and tracing are different and we have a problem when it comes to tracing things. If we could have some feature related to tracing between microservices or between any sort of logging, that would be nice.

We've been using the solution for three or four years. We've used it since 2019.

This is a very stable solution. It's reliable. There are no bugs or glitches. It doesn't crash or freeze.

It's scalable in the sense of pods or quantity or numbers of requests, yet not so scalable when considering persistence. We can't handle too much long-term data.

We have at least 500 people using the solution right now.

As a bank, we have some constraints around using and adding new tools. It's very difficult to change stacks. Therefore, we have no plans to stop using the solution anytime soon. 

I've never directly worked with technical support. We have our own support comprised of our own employees. I do not deal with external support services.

We did no previously use a different solution. 

I can't speak to the initial setup. The infrastructure team handled the setup. I did not implement it directly.

It is my understanding we needed three or four engineers to handle the deployment and maintenance process. 

I do not have any details about the cost or licensing. That said, the cost is public, and likely, someone can search for the approximate costs online. 

We are a customer.

I'm not sure which version we're using. I'm from the development team. The people who are doing the configuring work would know the version.

I'd rate the solution seven out of ten. It is a good solution, yet not quite perfect.

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 use the solution to monitor our website and APIs request and response cycle, also for log aggregation. We also used it for APM and searching for slow and database queries. 

It helped a lot in identifying bottlenecks and events happening simultaneously among several services, since we can aggregate the logs into a single repository of data

I am impressed with the product's Logstash. The tool is fast and customizable. You can build beautiful dashboards Kibana using Logstash as data source. It is useful and reliable. 

It was not possible to use authentication three years back. You needed to buy the product's services for authentication. 

I have been working with the product for three years. 

The tool itself is stable but depends on your infrastructure. If you have slow disks, the searches tend to take more time. If you need more data retention, be sure to keep an eye on disk space. Otherwise, the service crashes easily.

The tool's scalability is tied to your infrastructure. You need to have the money and resources to scale your infrastructure. To scale up, you need faster disks and more servers.  My company had 15 users using the product for a small API, and the cost was not so high.

The product's tech support is very helpful and skilled.

Positive

The product's setup is difficult, since you need at least 5 servers in a distributed topology to achieve its full potential: 3 machines for elasticsearch, 1 for logstash and another for kibana

In house

"The tool is an open-source product, but you have to self-host it and you need specialized personnel to maintain it.

If you are self hosting the solution, you need to take care of indexes and understand cluster sharding and distributed systems' election system

On-premises

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. 

Public Cloud

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. 

On-premises

We use the solution for log gathering, analyzing, and dashboard creation (with Kibana).

For example, several clients require the ability to store and search logs freely without the constrictions that would be in place if a traditional database was used. 

Elasticsearch is perfect for these use cases since it is a non-SQL database with advanced querying capabilities based on the Lucene search engine. 

There is excellent support and a large community that answers possible questions online in detail and very quickly. I was amazed at the help I got several times.

It gave us a tool to perform queries on unstructured data that had no fixed schema/form. This alone was a great asset, especially when dealing with clients that have large datasets from various sources that each follow their own format. 

It gives us the possibility to store and query this data and also do this efficiently and securely and without delays. 

Moreover, its learning curve was not steep. Therefore, no training was required - or at least no significant amount of time was consumed for training activities.

The ability to store unstructured data and perform fast searches that could be customized in detail is quite helpful. This is also a direct request from more and more customers. The Lucene search engine provides the needed speed. In larger projects with multiple nodes, disaster recovery and prevention is an asset (and it is needless to explain why). 

AI and machine learning capabilities have also emerged as a direct result of requests from customers. The addition of these features is useful and also can provide advanced security capabilities (such as tracking unusual behavior detection in logs).

Dashboards could be more flexible, and it would be nice to provide more drill-down capabilities. 

Although the discover function offers exploratory capabilities and one can search for various patterns in logs, the ability to do this from the dashboard function would be very useful. It would make the procedure more simple for the end user, and require less training. It would also be pretty much self-explanatory (drill down and explore specific parts of the diagram/dashboard). 

Also, more predictive analytics would be a nice-to-have feature.

I have been using the product for about two years.

The stability can be impressive.

The scalability is very good.

Technical support is excellent!

Positive

I have used Prometheus and Grafana. They do not offer the capabilities of ELK and their focus is different.

The setup is straightforward - although Logstash needed extra care in Windows VM installations.

We handled the setup in-house.

We have seen an ROI of 50% at least.

I'd advise people to involve a team with people from different departments in order to predict the correct scale.

Loki seems to be an alternative with fewer capabilities.

Logstash seems to have a very small capability to report errors, and that makes it difficult to troubleshoot. It would be nice to get some indication so as to save time.

Public Cloud