Elastic Search Reviews

We use it for locating and retrieving documents, particularly in scenarios where the data lacks a predefined structure. These documents may encompass various types of information, such as logs or other records.

It is highly valuable because of its simplicity in maintenance, where most tasks are handled for you, and it offers a plethora of built-in features.

Currently, their focus seems to be on expanding integrations and introducing more external tools, somewhat diverging from enhancing the core product. While integrating with tools like agents for ingesting data from sources like firewalls is valuable, I believe prioritizing improvements to the core product would be more beneficial. For instance, the development of a multi-step query engine could significantly enhance user experience. The ability to execute queries, receive results, and then perform subsequent queries based on those results is a fundamental feature that, while achievable through code, seems to be lacking as a built-in capability. While they possess a robust infrastructure, the current upgrade process isn't seamless and can result in downtime. As a customer, this can be frustrating, especially when there are methods like replicating to a new instance, performing the upgrade, and then transitioning back, which could potentially minimize downtime. This is crucial in a cloud service where ensuring availability is paramount, considering the significant investment in such services.

I have been working with it for two years.

It offers good stability capabilities.

It is a scalable tool, but it's not impressive. The challenge arises when scaling out becomes prohibitively expensive. Instead of offering end-users the flexibility to specify the number of instances, there's a tendency to provide preconfigured packages. This approach may not be ideal, particularly for those seeking smaller scale-ups.

Their documentation is commendable as it provides a clear understanding of their offerings. Also, the accessibility to their support further enhances user-friendliness, making it a straightforward and user-friendly experience. While it may be slow, their competence in what they do is evident. I would rate it eight out of ten.

The initial setup was straightforward.

Setting up the system initially is quite straightforward, but when it comes to upgrades, the process becomes more challenging. It was an in-house deployment. The primary focus is on designing the solution, considering factors like the importance of replication, cluster size, speed, and disk space. I appreciate their approach of guiding you through these considerations, making it easier to grasp the bigger picture. This initial design phase is a complex but crucial step. Once that's sorted, the subsequent steps are relatively straightforward—just a few clicks to establish the baseline. If you're working on a standard deployment, it's a hassle-free process.

The pricing structure depends on the scalability steps. It begins as quite affordable and maintains affordability for a while. However, there's a turning point where it transitions from being reasonably priced to becoming notably expensive.

We've explored a few alternatives, but I believe Elasticsearch, particularly with Elastic and Elastic Cloud, stands out as the current industry standard. Opting for a widely used platform is advantageous due to the larger community it attracts. A substantial user base means more people to consult, numerous information sources, and a wealth of case studies. While there are smaller, medium, and even large alternatives, having around eighty percent of the community share provides a significant pool of expertise and resources to tap into.

The main reason we opted for it is because the installation is straightforward, and maintenance is made easy as they handle that aspect for you. The extensive knowledge base offers substantial assistance, making it less reliant on individual expertise. I believe it's a solid product, especially for beginners. While it's not free, it's well-suited for more complex tasks. Keep in mind that for intricate functionalities, you might need to develop and create tools beyond what Elastic Cloud offers. If you're considering a cloud-based solution for schema-less documents, Elasticsearch is a solid choice. On the other hand, if you have the resources to handle on-premises installation, I would recommend it for companies with the capability to manage the deployment themselves. Overall, I would rate it eight out of ten.

Public Cloud

We save credentials, new account information, logs from Palantir Panorama, Firefox logs, traffic logs, GlobalProtect logs from our servers, and Active Directory new users. We're still improving this, but not very fast.

I appreciate that Elastic Enterprise Search is easy to use and that we have people on our team who are able to manage it effectively.

We are keeping an eye on other products like QRadar and Splunk in case they offer features that would benefit our company.

We currently use the free version of Elastic Search for some of our logs. However, if we were to use it more extensively, we would need to consider the pricing of the paid plans.

Another area of improvement is stability. 

 I have been using this solution for five years now.

I would rate the stability a seven out of ten. We faced a few issues. 

I would rate the scalability a seven out of ten. 

We don't use the support because we use the free version.

We were able to handle the deployment ourselves. We have one administrator and three users for this solution. So, there are four people in total. 

I use the free version. We use the free version for some logs, but not extensive use.

Overall, I would rate the solution a seven out of ten. The free version is not very useful.

On-premises

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. 

It's a cloud-based service. At that time, we were using AWS, so we could get the same Elasticsearch capabilities from AWS. It was mostly a PaaS service that we could access. We had the Elasticsearch specific server and database hosted on an AWS instance, and then we fed the data to it and tried to fine-tune the algorithm to give the necessary search intelligence that we needed.

We're not using the latest version. We're using a version that was released one year ago.

The whole organization has about half a million users, but at any point of time, a hundred users might be using it.

The AI-based attribute tagging is a valuable feature. It passes through text data and identifies the tag-words and keywords and connects them to various attributes in the whole system. The system was supposed to run through a lot of existing data in terms of which tag-words would reflect which keywords. There was a model built on top of that. We were building a machine-learning model, which passed through all of the data and did the necessary attribute tagging. We couldn't find attribute tagging in other services.

We initially tried to do it in-house, but we couldn't get the accuracy that we wanted. Elasticsearch was quite efficient in terms of getting accuracy with the limited amount of data that we had. We had 10,000 to 20,000 records. Based on that, we had a good amount of accuracy, which we were happy with. There's a lot we can do with customization.

The documentation regarding customization could be better. Other than that, Elasticsearch has very good documentation. We can get a lot of information from forums.

I have worked with this solution for six months. 

The solution is stable.

As far as what we could accomplish, it was scalable, but we didn't have a lot of data that needed to be processed. We had 10,000 records and it was scalable.

We have reached out to tech support when we have had queries, and they respond in time. We didn't have an escalation process, but we had a lot of queries.

We chose Elasticsearch because we could build a model in a short amount of time. It allows us to build a whole setup in one month and get 93% accuracy. Even if you look at the complex AI-based features that we built within a shorter span, we could build that model with high accuracy, which wasn't possible with other search enterprise vendors that we used.

Setup was a little complex, but we had in-house expertise.

The solution needs regular fine-tuning in terms of the data model. As we get more and more data into the system, the predictability and accuracy of the output keeps changing. On the application and DB side, it was fine. Not a lot of maintenance was required.

Deployment was done in-house.

The solution is affordable. Previously, we wasted a lot of time by building our own system, which we could have avoided by moving to Elasticsearch earlier.

I would rate Elasticsearch as eight out of ten. 

Elasticsearch provides a lot of possibilities. You need to understand your requirements and how Elasticsearch can fulfill them. Somebody might be looking at a simple keyword service or attribute tagging. If you don't understand exactly what you're looking for, you'll get lost in their options and waste a lot of time.

Public Cloud

Using real-time search functionality to support operational decisions has been helpful. However, it is not functioning correctly, as the real-time search consumes significant system resources.

The search feature is one of the valuable features of Elasticsearch.

There are areas for improvement in Elasticsearch.

The real-time search functionality is not operational due to its impact on system resources. There are some stability issues.

My overall experience with support was positive.

Neutral

The initial setup is complex.

I do not have specific details about the implementation team. The process might require certain expertise.

The pricing is not cheap and is expensive.

I compared the differences between Elastic and other SIEM solutions.

I am more like an implementer than a customer.

I'd rate the solution seven out of ten.

Other

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

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 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

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.