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ELK Elasticsearch OverviewUNIXBusinessApplication

ELK Elasticsearch is #1 ranked solution in top Search as a Service vendors and top Anomaly Detection Tools. IT Central Station users give ELK Elasticsearch an average rating of 8 out of 10. ELK Elasticsearch is most commonly compared to Amazon Athena:ELK Elasticsearch vs Amazon Athena. ELK Elasticsearch is popular among the large enterprise segment, accounting for 73% of users researching this solution on IT Central Station. The top industry researching this solution are professionals from a computer software company, accounting for 25% of all views.
What is ELK Elasticsearch?
Elasticsearch is a distributed, JSON-based search and analytics engine designed for horizontal scalability, maximum reliability, and easy management. Elasticsearch lets you perform and combine many types of searches — structured, unstructured, geo, metric — any way you want.
ELK Elasticsearch Buyer's Guide

Download the ELK Elasticsearch Buyer's Guide including reviews and more. Updated: November 2021

ELK Elasticsearch Customers
HotelTonight, Perceivant, Docker, Green Man Gaming, Xoom, AutoScout24, TheLadders, Center for Open Science, Parleys, Tango
ELK Elasticsearch Video

Pricing Advice

What users are saying about ELK Elasticsearch pricing:
  • "The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."
  • "We are using the open-sourced version."
  • "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 are using the free version and intend to upgrade."

ELK Elasticsearch Reviews

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Erik De Decker
Owner & director at Pulsar ICT
Real User
Top 20
Good processing power, very scalable, and able to handle all data formats

Pros and Cons

  • "There's lots of processing power. You can actually just add machines to get more performance if you need to. It's pretty flexible and very easy to add another log. It's not like 'oh, no, it's going to be so much extra data'. That's not a problem for the machine. It can handle it."
  • "The solution has quite a steep learning curve. The usability and general user-friendliness could be improved. However, that is kind of typical with products that have a lot of flexibility, or a lot of capabilities. Sometimes having more choices makes things more complex. It makes it difficult to configure it, though. It's kind of a bitter pill that you have to swallow in the beginning and you really have to get through it."

What is our primary use case?

We try to detect malicious files by the logs. The logs are all centralized including all our PCs, our callers, our servers, Linux, windows, Polaris names. We scan everything. Then we have pre-defined specific use cases that allow us to identify if there is an attack on the machine or indirectly by the endpoint. On top of that, we can check with users as we're not directly dealing with the configuration, so we can follow up on the alerts we receive. On top of that, we have the systems in place that allow us to detect if certain inexcusable items are on the system, such as malicious files. We can do this because we also retrieve the log files of the identifiers.

What is most valuable?

The fact that you can dump any type of format in the database without any specific reformatting is fantastic. It makes it very flexible in collecting information and that saves us a lot of time because otherwise, we would really need to define specifically what we're looking for and reformat everything. With this solution, that's not necessary. We can directly, and in a really standard raw format, dump the data into the database. Only afterwards do we need to define what specifically we're looking for, however, at that point, it's not a big deal to actually add an additional log and to collect additional information. 

The solution is very scalable. 

There's lots of processing power. You can actually just add machines to get more performance if you need to. It's pretty flexible and very easy to add another log. It's not like 'oh, no, it's going to be so much extra data'. That's not a problem for the machine. It can handle it.

What needs improvement?

The solution has quite a steep learning curve. The usability and general user-friendliness could be improved. However, that is kind of typical with products that have a lot of flexibility, or a lot of capabilities. Sometimes having more choices makes things more complex. It makes it difficult to configure it, though. It's kind of a bitter pill that you have to swallow in the beginning and you really have to get through it. 

Once you begin to understand the concepts and how to actually look for data it's a very pleasant solution, but the learning curve is very steep in the beginning, to the point that they could improve it to make it a bit less intimidating to start. There needs to be a bit more intuition behind the architecture and the data search.

For how long have I used the solution?

This solution has been used for at least five years at the company.

What do I think about the stability of the solution?

It's very stable. The only thing that might happen is that sometimes when you do a search it will stress the machine a bit too much. If that happens, then it's a matter of, if you do it the wrong way, the machine gets stressed and then it slows down. However, it will not crash. It almost never crashes. You'll simply figure out that the machine is overwhelmed and take the stress off. 

The problem, occasionally, is that it may become unresponsive, but it isn't really unresponsive, it's just that the system is overloaded. That can only happen if you do your database search in the wrong way. That's why, especially when you have a lot of data and are really concentrating a lot of data on a few machines, you have to be careful of what you're doing. 

It's a very nice tool but you have to be a bit aware of how to deal with this, especially when you have a lot of data and you have limited processing capacity. If you have unlimited processing capacity you can do whatever you want with it. I personally can say that I've never seen a machine crash.

What do I think about the scalability of the solution?

The scalability of the product is good. It's our key system that generates alerts and does surveillance on a security level. This product is extensively used in our organization.

We have people of course, from the server team that makes sure that the logs get collected. And then we have the people that actually deal with the configuration of the ELK as well. That is a team of five or six people that we use now. Then, of course, we have all the teams that follow up on the alerts, and there, I would say, we have two or three different teams, which is between 10 and 20 people. That's just part of the people that work with the solution.

How are customer service and technical support?

I work on part of the team that deals with technical support issues. There's a good community around the solution. This is because the product is actually open-source. With a lot of typical issues, you can simply Google questions and you will find the answer. Of course, we do have a support contract with the company. I don't deal directly with that, however. We contact them directly if we really need to and we have maintenance contracts with them. Unfortunately, I can't really speak to how good or bad they are because I've never called them myself.

Which solution did I use previously and why did I switch?

Before we switched over to this, we used it in combination with an end product called QRadar, but both of them together were time-consuming. 

How was the initial setup?

It's easy to install the servers, that's not really the problem. The difficulty is afterward. Users need to understand how to explore the data.

The server setup is the easy part. Even, let's say, moving the log into the machine or into the database is no problem. However, then you have all this data and you will really struggle to understand the information. That is sometimes not always obvious at the outset. In order to do that in an effective way, it requires a little bit of manipulating.

To install the servers, a minimum installation takes me a day or more. It's for the most part usually pretty fast.

What about the implementation team?

I myself have already had quite a lot of experience with the product. Therefore, I can set it up myself.  Most customers or most IT departments will struggle to set it up due to the difficult learning curve in the beginning. 

I would definitely recommend most users or companies, at least for the beginning, to get help troubleshooting problems. It will help them understand a little bit more about the steep learning curve. It really makes things much easier, and much more effective. 

Which other solutions did I evaluate?

I have used different products myself due to the nature of my work. I'm a security consultant. I have been working with different customers who use different solutions, which means that I have used other things and can evaluate and compare them for clients.

I've worked with Splunk, for example. Splunk, for instance, on the level of data mining and inquiring, might be easier. It's a bit more intuitive. The downside of it is as soon as you start collecting a lot of data, it becomes extremely expensive to use Splunk. It's a very good product. However, typically, with the need to collect as many logs and as much data as possible, Splunk becomes expensive, and you can't put it in a budget easily. It's simply out of budget for many as soon as they start clicking. Also, the purpose of a security system is not the same.

With Splunk, some will not add additional logs because they don't often have the budget, especially when it immediately means that you're going to need to increase your costs enormously. That's not the purpose of a security system. For the system to be effective you must be able to have good surveillance and that means that you should not hesitate in adding your logs. Still, when the costs double, people hesitate and if they don't have the budget and cut the logs, things can get through. Fortunately, with ELK, you don't have that issue. With ELK you don't pay for gigabytes, or terabytes or the data that you use. That's the main advantage compared to Splunk. But Splunk, it has a less steep learning curve.

What other advice do I have?

I'm just using it as a customer

We tend to use the latest versions of the solution. We try to upgrade it on a regular basis.

I'd advise other companies considering implementing the solution to get a team in that knows the product and try to take advantage of their knowledge. It will help reduce the pain of the learning curve.

I'd rate the solution eight out of ten.

I would not give it a ten because of the steep learning curve. I know what the product is, but many do not, and for them it will be quite difficult to get started without becoming very frustrated in the process. 

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Kiran BM
Chief Data Scientist at Everlytics Data Science Pte Ltd
Real User
Top 20
The go-to stack for machine- and sensor-generated data use cases. Easy to deploy and maintain. Elastic's ELK Elasticsearch, unlike AWS Elasticsearch, comes with batteries included.

Pros and Cons

  • "ELK Elasticsearch is 100% scalable as scalability is built into the design"
  • "The metadata gets stored along with indexes and isn't queryable."

What is our primary use case?

I'm involved in architecting and implementing Elasticsearch-based solutions, catering to various use cases including IIoT, cybersecurity, IT Ops, and general logging and monitoring.

The intention of this article is not to compare AWS Elasticsearch with Elastic ELK Elasticsearch and at the end declare the winner. Elasticsearch by itself is one of the coolest and versatile Big Data stacks out there. If you are planning to use it in your organization or trying to evaluate if it is the right stack for your product/ solution, this article offers some insights from an architect's perspective.

How has it helped my organization?

I'm not the right person to answer this question as I'm the service provider. My clients are the right people to answer.

What is most valuable?

The Spaces feature in Kibana is really useful. I can ingest all data and then offer multi-tenancy on a single stack to various departments (internal) or customers (external). This feature isn't available in AWS Elasticsearch, and Machine Learning isn't available either.

Other useful features such as Canvas (used to create live infographics) and Lens (used to explore and create visualisations using a drag-and-drop feature) are available only in Elastic's ELK Elasticsearch.

In the last 18 months Elastic has really caught up and also gone way beyond AWS by putting together all the missing components that make ELK Elasticsearch the most comprehensive stack in the entire Big Data ecosystem. Comprehensive because one stack addresses all of the three essential technical components of an end-to-end system: collect, store and visualise terabytes (and even petabytes) of structured or semi-structured data at ease.

What needs improvement?

Enhance the Spaces feature to make it fully multi-tenant by enabling role-based access control (RBAC) at a Space level rather than overall Kibana or stack level like it is currently.

Elastic needs to work on their Machine Learning offering because currently they have been trying to make it a black box which doesn't work for a serious user (a Data Scientist) as it doesn't give any control over the underlying algorithm. It's like a point-and-click camera vs a DSLR. The offering started with a single/ univariate anomaly detection on time-series data. Now, they have a multivariate which is good, but beyond this, we cannot build any other Machine Learning models, like traditional supervised models. Anomaly detection uses mostly unsupervised algorithms and also it is a very broad problem space for a black box to solve it fully.

Make index’s metadata searchable (or referenceable in search queries).

For how long have I used the solution?

5 years

What do I think about the stability of the solution?

Elastic ELK Elasticsearch is one of the most stable Big Data engines and the simplest to maintain and scale. Redundancy is built into the design so there is no single point of failure. We can configure a DR easily and if something goes wrong, we can restore the system into a brand new cluster in hours.

What do I think about the scalability of the solution?

Elasticsearch by itself is 100% scalable as scalability is built into the design like any Big Data system. We just have to add more nodes, and it scales horizontally and then redistributes the data into the new nodes, and the cluster becomes faster and agile automatically. Cross-cluster replication comes with a Platinum license. But this feature is highly exceptional and not a common need.

Which solution did I use previously and why did I switch?

I have worked with all the flavours of Elasticsearch viz. Elastic.co's ELK which is popularly known as the ELK stack (pronounced as 'yelk'), AWS Elasticsearch and Open Distro plugins for Elasticsearch.

All (including Solr that comes with Hadoop) are built on a common underlying technology, Apache Lucene. The difference is the added features that I call 'batteries included'. To be precise, Elastic's ELK Elasticsearch, unlike others, comes with free enterprise-grade apps (called plugins in Kibana) and a bunch of cool and useful Kibana features. It also features a good deal of engineering automation conveniences built into the stack.

Did you know that the original founders of Elasticsearch are the folks at Elastic.co, the company that has recently transitioned to an open-core philosophy by design. But since AWS took the initial lead and started offering the stack as AWS Elasticsearch service it became more popular and a preferred option for the uninformed. Elastic, on the other hand, was busy innovating and adding more muscle to the stack that it is no more limited to being just the fastest search engine on the planet. In fact, the keyword ‘search’ in Elasticsearch is not relevant anymore and, moreover, it is misleading.

How was the initial setup?

Initial setup is indeed straightforward and fast because it will mostly be a single-node cluster. But as the data volume grows and we start seeing a performance lag, the stack requires scaling (by adding more nodes) and a professional intervention for doing the right capacity design and configuration fine tuning.

What about the implementation team?

It is always a good idea to engage a professional vendor to implement it right the first time and save yourself a lot of time in experimenting and trying to figure out the optimisation hacks and how-to’s all by yourself.

What was our ROI?

A stack like Elasticsearch that enables heavy lifting of the data effortlessly comes with its intrinsic yet obvious ROI. If one is not able to realise the ROI it means either the data is bad (garbage in, garbage out) or the stack is not implemented properly.

What's my experience with pricing, setup cost, and licensing?

The basic license is free, and it comes with a lot of features that aren't supposed to be free! With a Gold license, we get Alerting (called Watcher) and some modest enterprise features. Note that if alerting is a must feature for you, you can install open-source alerting plugins like Open Distro Alerting or ElastAlert and avoid the Gold license cost. Active Directory integration, SAML, SSO, Machine Learning etc. come with Platinum license. The licensing is per-node and per-annum basis for an on-premise installation and for Cloud Elastic-managed service the cost is baked into the hourly pay-as-you-go fee. Kibana does not have a license, so it's free.

If you don't want alerting, Active Directory or LDAP integration and are good with native authentication, the basic license will suffice. The basic license also comes with many internal stack features, which are free. For example, data segregation into hot and warm storage, automatic configuration, and rolling over the index after achieving a certain size limit. 

SIEM (Security Information and Event Management) app is free. Also is another cool app called Uptime that helps us monitor the uptime of servers and web services. We can do this without any third-party licensing cost. Just turn on the apps, ingest data using Beats and the apps will start thriving. Over time they become mission critical to your business.

For example, the SIEM app will automatically populate the dashboards and allow us to monitor network traffic, successful logins, unsuccessful login attempts, and anomalous security events. All that comes off the shelf and is free. You'll pay a lot, on the other hand, for a traditional SIEM like ArcSight or LogRhythm.

Another free app called Infrastructure (formerly known as Metrics) helps monitor the server infrastructure by configuring light-weight data collectors called MetricBeats (for Windows systems) and AuditBeats (for Linux systems). The Beats will start pumping in all the system performance metrics into the stack and help monitor the memory, CPU and disk utilization.

Which other solutions did I evaluate?

I have worked with all the flavours of Elasticsearch viz. Elastic.co's ELK which is popularly known as the ELK stack (pronounced as 'yelk'), AWS Elasticsearch and Open Distro plugins for Elasticsearch.

All (including Solr that comes with Hadoop) are built on a common underlying technology- Apache Lucene. The difference is the added features that I call 'batteries included'. To be precise, Elastic's ELK, unlike the others, comes with free enterprise-grade apps (called plugins in Kibana), a bunch of cool and useful Kibana features, and a good deal of engineering automation built into the stack.

Moreover, the original founders of Elasticsearch are the folks at Elastic.co, the company that's built on open-core philosophy. But AWS took the initial lead and offered the stack as AWS Elasticsearch service catering mostly to search-engine use cases. But ELK, with all its goodness, is much more than a search engine! In fact, the keyword search in Elasticsearch is very misleading.

What other advice do I have?

You can spin up Elastic ELK Elasticsearch fully-managed service either on AWS, GCP, or Azure, or have your own on-premises installation and dockerize it. Whereas the AWS Elasticsearch is available only on AWS. That's the hosting difference.

Elastic ELK Elasticsearch comes with a support-only subscription, and there are a lot of updates happening. Kibana is constantly improved and there’s a new release every two weeks.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Learn what your peers think about ELK Elasticsearch. Get advice and tips from experienced pros sharing their opinions. Updated: November 2021.
552,305 professionals have used our research since 2012.
Kiran Raparti
Head of Technology Operations at a financial services firm with 11-50 employees
Real User
Top 20
Open-source with good community support but number of search queries is limited

Pros and Cons

  • "The most valuable feature is the out of the box Kibana."
  • "I would like to be able to do correlations between multiple indexes."

What is our primary use case?

I run the function to review the usage for the team and for the organization itself.

We use this product internally and then some of our business relationships with the other businesses that we have, they get their data from our data. It's more for collaborative data reporting that we have with them.

What is most valuable?

The most valuable feature is the out of the box Kibana. You plug it in and start the basic analysis on the data out of the box. This also gives a quick way to check the data and the models to figure out what fits the needs.

What needs improvement?

There are a few things that did not work for us. 

When doing a search in a bigger setup, with a huge amount of data where there are several things coming in, it has to be on top of the index that we search. 

There could be a way to do a more distributed kind of search. For example, if I have multiple indexes across my applications and if I want to do a correlation between the searches, it is very difficult. From a usage perspective, this is the primary challenge.

I would like to be able to do correlations between multiple indexes. There is a limit on the number of indexes that I can query or do. I can do an all-index search, but it's not theoretically okay on practical terms we cannot do that.

In the next release, I would like to have a correlation between multiple indexes and to be able to save the memory to the disk once we have built the index and it's running.

Once the system is up, it will start building that in memory.

We need to be able to distribute it across or save it to have a faster load time.

We don't make many changes to the data that we are creating, but we would like archived reports and to be able to retrieve those reports to see what is going on. That would be helpful.

Also, if you provide a customer with a report or some archived queries, that the customer is looking at when they are creating, at first it will be slow while putting up their data or subsequently doing it. I want it to be up and running efficiently. 

If the memory could be saved and put back into memory as it is, then starts working it would reduce the load time then it will be more efficient from a cost perspective and it will optimize resource usage.

For how long have I used the solution?

I have been familiar with this product for approximately four years.

What do I think about the stability of the solution?

ELK Elasticsearch is stable.

What do I think about the scalability of the solution?

It's scalable, but there are some limitations.

If you are scaling a bit too quickly, you tend to break the applications into different indexes. 

The limitations come in when getting the correlation between the applications or the logs.

It is difficult to get the correlations once the indexes have been split.

How are customer service and technical support?

We are using the open-source version, that is installed on-premises.

We have not worried about technical support, but the community is good.

Which solution did I use previously and why did I switch?

Before ELK, we used another solution for internal usage, and also, we used Splunk for different use cases in a different organization altogether.

It wasn't a switch per se, it was a different organization with a different use case.

How was the initial setup?

The initial setup is simple, not too difficult. 

Getting the index, doing your models, and putting the data in, correctly, is done more on a trial and error basis. You have to start early and plan it well to get it right.

What's my experience with pricing, setup cost, and licensing?

We are using the open-source version. 

We are not looking into the subscription because it's on-premises in-house.

What other advice do I have?

For anyone who is looking into implementing this solution, the only tip is to get your models for the type of actual use that you are looking at upfront in order to have a good run.

I would rate ELK Elasticsearch a seven out of ten.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
Murat ERAYDIN
Owner and CEO at Karmasis
Real User
Top 20
Good search speed and easy to deploy, but complicated to scale and needs an ODBC driver and better licensing

Pros and Cons

  • "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."

What is our primary use case?

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.

What is most valuable?

The search speed is most valuable and important.

What needs improvement?

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.

For how long have I used the solution?

I have been using this solution since version 1.0.

What do I think about the scalability of the solution?

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.

How are customer service and technical support?

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.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

Its initial setup is straightforward. There were no problems.

What's my experience with pricing, setup cost, and licensing?

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.

Which other solutions did I evaluate?

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

What other advice do I have?

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Uwe Wächter
Senior Consultant at a tech services company with 10,001+ employees
Real User
Top 20
Stable, offers good value for money, and requires very little maintenance

Pros and Cons

  • "The initial setup is very easy for small environments."
  • "There are a lot of manual steps on the operating system. It could be simplified in the user interface."

What is our primary use case?

Our main use case is to centralize all the logs from the infrastructure environment and the data center.

What is most valuable?

The most valuable aspect of the solution is the visualization with Kibana. What we have not yet started, yet, we plan to do, is to use machine learning.

The initial setup is very easy for small environments.

There is very little maintenance needed.

The solution is stable.

The scalability is good.

The solution offers good value for the price.

What needs improvement?

They could simplify the Filebeat and Logstash configuration piece. There are a lot of manual steps on the operating system. It could be simplified in the user interface.

For how long have I used the solution?

I've been using the solution for about a year at this point.

What do I think about the stability of the solution?

The stability is really good. We use it in a fully virtualized environment, and that's not a real recommendation from Elastic. However, even with how it's stored, even if it's not a recommendation, for this small environment we have here, it's stable enough. It's working.

What do I think about the scalability of the solution?

We're in the very early stages of usage. We only have maybe 20 people on the solution currently. We are increasing this, however. There will be more.

The solution is easy to scale. You can add new Elasticsearch clusters. It should be noted that you have to separate the different roles from Elasticsearch to other devices, so you need a little bit more knowledge to do it right.

How are customer service and technical support?

We've been in touch with technical support a little bit as we're still in negotiation. Right now, we are running the basic product which is free of charge. We're in negotiation with the vendor for a license, where we will get proper support. We need it.

Which solution did I use previously and why did I switch?

I'm also familiar with Splunk, which is more expensive.

How was the initial setup?

In our case, it was a simple installation process. It was just set up in small environments, however, if it's getting larger, it will be more complex as then you have to split all the different roles onto different machines, to get the performance you need.

Therefore, for small environments, it's very easy. If you're doing a big environment, then it's much more complex.

The only maintenance needed is for updating the systems. We're working on it to make it all more or less automatic. All we need to do is to implement the updates when they arrive.

What about the implementation team?

We just handled the initial setup internally. We did not need the assistance of any integrators or consultants. 

What's my experience with pricing, setup cost, and licensing?

It's a bit too expensive, however, it's not as expensive as Splunk, which is a good thing. It's okay. There are cheaper products that we know, however, this is a very rich product, and it's got a very wide functionality, and a wide range of functionalities which I don't see in the other products, especially not in the cheaper ones.

What other advice do I have?

I'm just a customer and an end-user.

Our company is always using the latest updates.

I'd advise new users that you need to do a POC or get a test installation. It's free of charge. It's important to ingest a lot of data so that you get a feeling of scalability and performance. To put something in your lab, for example, is very helpful. It's only when you have data in the system, that you can see the benefits of the Elastic environment.

I would absolutely recommend the solution to others. I'd rate it at a nine out of ten. I've been pleased with its capabilities overall. 

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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HY
Manager at a tech services company with 11-50 employees
Real User
Helps us keep firewall logs and collect traffic flow information

Pros and Cons

  • "The product is scalable with good performance."
  • "The GUI is the part of the program which has the most room for improvement."

What is our primary use case?

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

What is most valuable?

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

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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.

What do I think about the scalability of 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.

How are customer service and technical support?

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.

How was the initial setup?

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.

Which other solutions did I evaluate?

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.

What other advice do I have?

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
CN
Senior DevOps Engineer at a financial services firm with 10,001+ employees
Real User
Offers certain log filtering capabilities and we can vet what we push into our database

Pros and Cons

  • "The solution is quite scalable and this is one of its advantages."
  • "There is an index issue in which the data starts to crash as it increases."

What is our primary use case?

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.

What is most valuable?

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. 

What needs improvement?

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.

For how long have I used the solution?

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

What do I think about the stability of the solution?

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

What do I think about the scalability of the solution?

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

How are customer service and technical support?

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.

How was the initial setup?

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. 

What other advice do I have?

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.

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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DE
Cyber Security Professional at Defensive Cyber Security Center Germany
Real User
Easily customizable dashboard and excellent technical support

Pros and Cons

  • "Dashboard is very customizable."
  • "Could have more open source tools and testing."

What is our primary use case?

In terms of use case, we combine a lot of things with Elastic. It's two platforms, so with Elasticsearch, we're using the Beats, Kibana, and Suricata. It's a query engine and we use the information from our sensors. It gets ingested into that and we use the resources to get everything put on our dashboards. If something is detected, alerts come up right away and it's very, very accurate. The more ingest it receives, the better we can respond to threats. It's not just Elastic or Logstash, it's a combination of those and other tools that we would apply towards our threat detection and prevention. We have a partnership with ELK.

What is most valuable?

The company provides excellent technical support and wonderful engineers, even their sales engineers are great. The dashboard is a valuable feature - it's awesome and very customizable. 

What needs improvement?

I would like to see more open source tools and testing as well as a signature analysis in the solution. I think that a lot of times when we go into a corporate environment where it becomes more add on features or an additional service fee, it typically draws away from that product. 

I think it would be cool if they could provide a couple of licenses that would be test bed licenses so that engineers and people with have their hands on the keyboard could test any new development. 

For how long have I used the solution?

I've been using this solution for three or four years. 

What do I think about the scalability of the solution?

It is a very scalable soluton. It is very easy and I would recommend it to anyone. In terms of users it's all tiered. Most things are from tier zero at egress point of any major large-scale network all the way down to the customer. We have roughly 200 users. And those would include analysts and real time threat analysts. 

How are customer service and technical support?

I'm very satisfied with the technical support and would rate it highly. Sometimes there are issues because we are overseas and there is a six hour time difference which creates a lag. It's hard to get around that but they're very responsive. 

How was the initial setup?

We had issues when we first did the initial setup, because our resources were limited because it was a test that it was a proof of concept. It meant the initial setup was somewhat resource intensive. The data NGS itself was an issue when we were trying to filter and pull that information. Again, a signature analysis would have been helpful here.

What other advice do I have?

For anyone considering implementing this solution, I would say take a good hard look at your own infrastructure resources and scalability as you have to future proof everything. Whether it's scale or increase in customers building up through your actual hardware and your network infrastructure. You need to know it's capable of performing the tasks needed, because sometimes you outgrow yourself. So, I would say look at your resources and how it can be scaled.

I would rate this solution a nine out of 10. 

Which deployment model are you using for this solution?

On-premises
Disclosure: My company has a business relationship with this vendor other than being a customer: partner