Elastic Search Reviews

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.

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

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

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

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

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

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

It is easy to set up.

The solution scales well. 

They have great integrations on offer. 

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

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

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

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

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

We have maybe 200 people on the product right now. 

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

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

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

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

I'd rate the solution nine out of ten. 

I'm a customer and end-user. 

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.

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. 

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. 

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

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. 

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. 

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.

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. 

I am an end user, and we use Elasticsearch for our logs. Specifically, we use it for security logs for our enterprise, including machines, networks, and endpoints, as part of our IT infrastructure.

We have been able to collect our live logs, which helps us run security operations more effectively. It has enabled us to identify false positives and detect real-time malicious activities in the network.

The security portion of Elasticsearch is particularly beneficial, allowing me to view and analyze security alerts. It serves as a query engine for the database, enabling us to analyze logs for potential threats.

An improvement would be to have an interface that allows easier navigation and tracing of logs. The current system requires manually inputting dates to verify alerts. A visual timeline that pinpoints possible anomalies would be beneficial.

I have been using Elasticsearch for approximately one year.

I would rate the stability of the solution as nine out of ten. It is very robust.

I would rate the scalability as either nine out of ten. It's a very robust solution.

I do not interface directly with technical support from Elastic. Another colleague manages that aspect.

Positive

We did not use any different solution before Elasticsearch.

I was not involved in the setup process. Our architects and technical officer managed it.

I am not directly involved with pricing or setup costs. While I know a portion is open-source, a paid version might be necessary.

It was not my duty to evaluate other options. The architects and chief technical officer handled those decisions.

For someone wanting to be a security analyst, Elasticsearch is a valuable tool. It helps organizations collect large amounts of logs from various platforms like Windows, Ubuntu, and Palo Alto Networks.

I'd rate the solution eight out of ten.

We use Elasticsearch as an alternative to Splunk. It is basically for log monitoring.

It's probably a cost-efficient alternative to Splunk. The search interface is nearly the same. When it comes to visualizations, Elastic is a bit better than Splunk.

Elastic Search needs better guides for developers. Better guides for development.

I have been using it for a year.

I would rate the stability an eight out of ten. 

It's fairly scalable. I would rate the scalability of this solution a ten out of ten. 

There are around five end users using it in my team. 

Till date, we did not have any issues with  customer service and support. Like, initially, we had issues in accessing the portal. But that was the only issue, but it was resolved pretty quick.

The initial setup is fairly simple. Initially, it was on-prem, but right now, it's on the cloud.

It is pretty easy to integrate as well.

It's like, when someone is buidling products for scale, it reduces the time to market.

I would rate the pricing a seven out of ten, with one being high price and ten being low price. It could be cheaper for certain use cases, but since it gets the job done, no complaints for the pricing. 

Overall, I would rate it a nine out of ten. I would definitely recommend it to other users. 

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.

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.

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.

I have been using this solution since version 1.0.

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.

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.

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.

Its initial setup is straightforward. There were no problems.

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

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.

We have data in different databases. One is a relational database, and another is NoSQL. They are different services. They host document-like data. We used Elastic to convert the data structurally. We used Elastic as a multi-service search engine. It is a good solution. It is too powerful.

I would advise anyone to use the product. It is good. Data indexing of historical data is the most beneficial feature of the product.

The solution must provide AI integrations. I could direct my data flow to my AI tools if I use Elastic for IoT data.

I have been using the solution since 2007.

I rate the stability an eight out of ten.

The solution provides powerful scalability. I rate the scalability a ten out of ten. Our clients are medium-sized businesses.

I do not need technical support because the product works well.

The initial setup was very easy. I rate the ease of setup an eight out of ten. The setup can be done within minutes.

I use the community version. The premium license is expensive. I rate the tool’s pricing an eight out of ten.

With the power of Kibana, we can easily and dynamically analyze and summarize our log data. The internet has information about all the technical solutions. I bought some courses from Udemy for Elastic Search. I also got some documents from Elastic Search. The documentation for Java is very good. It was sufficient to learn as a developer.

I could integrate my products to Elastic Search easily. I use the default index for my solution, and it works very well. Elastic’s indexing policies are very good. I do not need any indexed operations for my solution. Overall, I rate the tool a nine out of ten.