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.

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.

We use the solution for search engines and indexing.

The solution is valuable for log analytics.

The solution's integration and configuration are not easy. Not many people know exactly what to do.

I have been working with the product for five years.

The product's deployment took a couple of days to complete.

The product's deployment was done in-house by myself.

I would rate the product a nine out of ten.

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.

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