The main use cases for Elastic Search are index building and retrieving information using Elastic Search vector, vector search, and related functionalities. Search is the primary use case.
ElasticCloud(Elasticsearch, FedRAMP, SaaS Contract) [Private Offer Only]
Carahsoft Technology Corp.External reviews
32 reviews
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Boosted search efficiency through real-time querying and seamless indexing for high-volume product data
What is our primary use case?
What is most valuable?
Computation is very good. The scalability is very good because we have a huge customer database that is searching lots of products, and auto-scaling or load balancing are the prominent features we are using in this.
If we look at the impact on operational efficiency, we can see that decision-making has become much faster due to real-time data and quick responses. We have also implemented many automations, which enhance our processes. For example, when we optimize certain fields to improve search functionality, it yields great results.
What needs improvement?
I have not explored Elastic Search at the most. Searching from vector DB is available in Elastic Search, and there is one more concept of graph searching or graph database searching. I have not explored it, but if it is not there, that would be an improvement area where Elastic Search can improve.
For how long have I used the solution?
I have been working with Elastic Search for more than two years.
What do I think about the stability of the solution?
It is very reliable, and it has no downtime.
What do I think about the scalability of the solution?
I believe it is scalable. Every day, we have thousands of users continuously utilizing the search feature. We haven't encountered any problems so far, and there is the potential for auto-scaling. It is currently a scalable solution.
How are customer service and support?
We have not contacted them yet. So far, we haven't had any need.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup is straightforward.
What about the implementation team?
We have a team of developers, so it is internally managed.
What was our ROI?
We have not calculated the ROI for Elastic Search, but we are a consumer platform where numerous searches are happening, and we are getting very good results from the current infrastructure of Elastic Search. Though the exact numbers or ROI were never calculated, the performance has been beneficial.
What's my experience with pricing, setup cost, and licensing?
It is average compared to other platforms. There isn’t anything particularly special about the pricing. However, the pay-as-you-go model is advantageous for the organization, as we only pay for what we utilize.
What other advice do I have?
We are using AWS for our solutions. In AWS, we are heavily using Redshift and Glue. We focus more on vector searches and boosting the keywords, and all those features we are using heavily. In search, the key parameter that we boost up during indexing is essential.
We self-implement Elastic Search in our e-commerce application. We are not currently doing a regex setup for RAG Playground, but there is a plan to do that. We are more into vector searches when it comes to how effectively the hybrid search capability meets our needs for combining traditional keyword and vector searches.
Regarding the workflow, we are using the API for real-time inference because lots of data is being loaded at real-time on the application, and it is working well for us.
I can definitely recommend Elastic Search to be used wherever you have consumer search capabilities needed in a large or scalable manner because it is very effective.
I would rate Elastic Search an eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Simplified agent deployment and highly responsive support
What is our primary use case?
My main use case is for security, specifically for the SIEM aspect, as I work as a cybersecurity engineer.
We specifically use this system for security-related topics. We have a dedicated environment for Large Language Models (LLMs). We have connected our LLM, but our primary focus remains on security. When we encounter any incidents or need to gather information about connected IPs, we rely on established rules and alerts. We utilize the chat functionality of this LLM to generate queries in Kibana language.
What is most valuable?
My favorite feature is the ease of use, particularly in how you integrate the agent. I've been using it since version 7, and we're on version 9 now, and I've seen the progress from using Beats to using the agent, making it so simple today to enroll a server with the Elastic Agent.
What needs improvement?
Deploying the Elastic Agent internally is relatively straightforward; it only requires a few commands to be run on the server. However, to manage this deployment at scale, we needed to develop a solution using Ansible. This involved creating scripts to install, restart, and uninstall the agent. While I would have preferred if Elastic had provided an official solution for these tasks, they haven't yet developed one that addresses all the necessary aspects. As a result, we've taken it upon ourselves to create these tools internally.
There are two areas in which it could improve. One is the smoother enrollment process for 1,000 or 2,000 servers at the same time, rather than having to develop something internal.
The second topic is the actual support of YARA rules—it's Y-A-R-A, which is specific for security. As of today, this is not supported, and I've been asking for a while now; I'm unsure if they will ever release it.
For how long have I used the solution?
I have been using this solution for at least four years.
What do I think about the stability of the solution?
I haven't seen any downtime.
What do I think about the scalability of the solution?
It is really scalable. Since we're on the cloud, whenever we need to upgrade or add resources, they handle everything. It takes a couple of hours due to the amount of data we have, and I've never faced any issues during upgrades.
How are customer service and support?
I have contacted technical support because we encountered issues when we started using the Elastic integrations, some of which were not finalized on their side. I had countless meetings with engineers from Elastic, including product managers and support engineers, to work on and fix the integrations we wanted to use. They have always been really responsible and responsive to my requests. Once, we had an issue with GCP, Google Cloud Platform, and they even sent us a complimentary five or six hours with an Elastic consultant to help set things up.
I would give them a nine out of ten because they are very responsive. They clearly know what they are talking about. I never encountered a situation where the support team didn’t understand what we needed.
How was the initial setup?
The initial setup process took around a month.
What they need is to be more transparent about the actual setup of the cluster and the deployment process. When using Elastic out of the box, there is information that is not readily available, requiring users to dig deep into the documentation to truly understand how it works. If you're looking to set up the cluster automatically, it works well for testing purposes. However, when installing two thousand servers at once, if your deployment isn't large enough, it can lead to crashes. Occasionally, we have to delete the logs just to access the interface. Therefore, I believe they should provide clearer guidance on using the deployment manager effectively.
We started four years ago with 200-300 servers, and now we are at around 2,000 servers. The learning curve involved understanding how it works, doing labs, and the difference between Elastic Search and competitors. Elastic really helped with support; we had weekly sessions with engineers from their side to assist us in setting up.
Maintenance on my end is limited to updates. Since we are using Elastic Cloud, they take care of the infrastructure.
What's my experience with pricing, setup cost, and licensing?
I am familiar with the pricing, as we negotiated it last year. Compared to other tools, it's fair. However, if we are talking with full transparency, Elastic pushes clients to buy the Enterprise edition instead of the Premium edition, and we don't see the value in that other than to spend more money more quickly. So, while pricing is good and what we expect to pay for this type of product, I'd love to finalize this concern.
Which other solutions did I evaluate?
We've tested multiple open-source tools based on Elastic before signing with them, including one tool called Wazuh that is built on top of Elastic. We've also tested the open-source edition of Elasticsearch where we manage the cluster and Splunk. Overall, I believe Elastic Cloud is still one of the best products out there.
What other advice do I have?
I would rate this solution an eight out of ten.
Searches through billions of documents have become impressively fast and consistent
What is our primary use case?
Our main use case for Elastic Search is primarily for application search and document discovery.
We built an application with APIs that make documents available for search to the enterprise and we store the documents as well. A typical flow would be when an upstream application delivers a document to us, and then a different application or different user looking for some documents comes to our application, enters the metadata for that document, which we use to search in Elastic Search to retrieve the document and then deliver that document to the end user.
What is most valuable?
The seamless scalability is something I see as among the best features Elastic Search offers.
The speed with which Elastic Search is able to search through all of the documents we place into it is quite remarkable, as we search through 65 billion documents in less than a second in most cases, on a constant consistent basis.
I find configuring relevant searches within Elastic Search platform very straightforward. Elastic Search is easily scalable.
The customer support for Elastic Search is quite good.
I advise others looking into using Elastic Search to think about the future of your platform and where you intend it to be in five years, and based on that, which version of Elastic Search best suits the needs of your platform. Additionally, jump into the AI products first as you're in the planning phase so that as you're filling out your data, the AI products and machine learning products can enrich the data real-time early on in the process, which will save you a lot of time later.
The overall performance of the platform, scalability of the platform and other additional features, especially when it comes to AI, really earn the nine.
What needs improvement?
The ability to change field types seamlessly would be a huge improvement for Elastic Search, and more seamless upgrades would also be a big improvement, especially with regards to upgrading between major versions.
The upgrade experience and inflexibility with fields keeps Elastic Search from being a perfect 10.
For how long have I used the solution?
I have been using Elastic Search the whole time I have been at Optum since 2019.
What do I think about the stability of the solution?
Elastic Search is stable.
How are customer service and support?
The customer support for Elastic Search is quite good.
I would rate the customer support a nine.
Which solution did I use previously and why did I switch?
We previously used a self-hosted Elastic running on virtual machines, and we switched to Elastic Cloud on Kubernetes at the urging of Elastic Search itself, as well as an internal drive towards cloud-first technologies. The features of Elastic Search Cloud on Kubernetes seemed to mesh well with the overall goals of our organization.
How was the initial setup?
My experience with pricing, setup cost, and licensing for Elastic Search is overall fairly straightforward.
What was our ROI?
I do not have any specific numbers on a return on investment, but I do have a general sense of the overall improvement of efficiency of the platform as we moved from on-prem hosted to Elastic Cloud on Kubernetes, where the time saved from maintaining the platform itself was significant.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing for Elastic Search is overall fairly straightforward.
What other advice do I have?
We have tried the hybrid search capability, and we have seen overall fairly positive results, though we have yet to roll it out in production.
We have implemented a proof of concept using Inference APIs in our processes, but we have yet to release it into production.
To be clear, we are not on Elastic Cloud serverless; we are on Elastic Cloud on Kubernetes, running on the Azure platform self-hosted.
We have not utilized Better Binary Quantization, BBQ, in our operations.
On a scale of one to ten, I rate Elastic Search a nine out of ten.
Machine learning features have improved search projects and user experience
What is our primary use case?
We use Elastic Search for search purposes and things related to semantic search.
It is not being used for the moment regarding my main use case for Elastic Search.
What is most valuable?
In my experience, the best features Elastic Search offers are its stability and brand new features that I consider very interesting.
The machine learning features of Elastic Search are very interesting, including the possibility to include models such as ELSER and different multilingual models that let us fine-tune our searches and use them in our search projects.
The machine learning features of Elastic Search have helped us with many things such as improving our searches and experience for the guests.
What needs improvement?
We could benefit from refining the machine learning models that we currently use in Elastic Search, along with the possibility to integrate agents, intelligent artificial intelligence, form of agent, and MCP.
It would be useful to include an assistant into Kibana for recommendations, advice, tutorials, or things that can help improve my daily work with Elastic Search.
For how long have I used the solution?
I have been using Elastic Search and Kibana for about four years.
What do I think about the stability of the solution?
In my experience, Elastic Search is quite stable.
What do I think about the scalability of the solution?
The scalability of Elastic Search is very good in my opinion. It never has incidents that cause issues in our daily tasks.
How are customer service and support?
The customer support for Elastic Search is one of the best I have ever tried. Whenever I had to create a new incident, I got the responses that I needed.
How would you rate customer service and support?
Positive
What other advice do I have?
I consider Elastic Search a very good project. On a scale of 1-10, I would give it a 10.
The features and capabilities that Elastic Search provides are very easy to use, and the documentation is rich. You can find and understand everything here to use it properly.
I would tell others looking into using Elastic Search that they can try it and see if it fits their use cases.
Elastic Search is a very good product. I really appreciate all the features that it provides, and I hope this product continues its evolution in the way it has been.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Search efficiency improves with enhanced metadata and log management
What is our primary use case?
At Shopee, I worked with numerous database schemas to find out which table columns belonged to which schema. We utilized Elastic Search to manage metadata for millions of tables, allowing us to search efficiently. Besides that, we used Logstash to put all the log files in Elastic Search for easy searchability.
How has it helped my organization?
Elastic Search significantly improved my work. Previously, when searching for text that appears in the middle of strings, the process was time-consuming. Elastic Search enables efficient searching, enhancing system performance and responsiveness. I can also collect logs through Kafka, send them to Elastic Search, and create indices, thus managing logs and customizing searches easily.
What is most valuable?
Elastic Search provides features such as stemming and range-based queries to search log files efficiently. It allows filtering data easily by searching for specific words based on created indexes. This made searches very efficient, and it also allows for log collection through Kafka and helps with managing logs and customizing searches according to needs, such as grouping by dates or user IDs.
What needs improvement?
Elastic Search could improve in areas such as search criteria and query processes, as search times were longer prior to implementing Elastic Search. Elastic Search has limitations for handling huge amounts of data and updates, especially if updates are frequent. It doesn't handle big data scale efficiently, especially regarding data size and scale, compared to Apache Solr. It doesn't support real-time search effectively, as it refreshes the indexes every few seconds.
What do I think about the stability of the solution?
It is stable as many companies already use Elastic Search. In cloud scenarios, it manages well by scaling up or down based on peak traffic. Otherwise, similar functionality needs to be replicated in a private cloud, including backups.
What do I think about the scalability of the solution?
Elastic Search requires enhancements for handling huge amounts of data and updates. Segmenting or sharding data and complexities regarding the cluster can be issues. Updating in Elastic Search involves index computations and user dependencies. There might be issues regarding data size and scaling, but these can be tuned and improved.
Which other solutions did I evaluate?
I remember Apache Solr, which is generally used for much larger scale data compared to Elastic Search. Apache Solr is used by most companies, and while Elastic Search is very common, there are technologies similar to Elastic Search, though I'm not familiar with all the names.
What other advice do I have?
I have used Elastic Search, but I might not be aware of many internal details; I just used the API to create an index, manage data, and search. It's very useful. On a scale of 1-10, I rate it an eight.
Log management capabilities impress but setup presents challenges
What is our primary use case?
The main use case for Elastic Search is mainly for log management.
What is most valuable?
I appreciate the indexing capabilities and the speed of indexing in their product, which demonstrates how quickly logs are collected and stored. The search capabilities are also valuable.
What needs improvement?
The architecture of Elastic Search could be improved as it is complicated for most general users to build up the environment and maintain the cluster.
Currently, I do not have suggestions for additional functions that could be added to the product.
For how long have I used the solution?
I have been working with Elastic Search for about two years.
What was my experience with deployment of the solution?
I usually use Elastic Search on-premises, which introduces complexity in deployment. Using the cloud version would reduce the complexity of setting up.
What do I think about the stability of the solution?
I would rate the stability for Elastic Search as eight out of ten.
What do I think about the scalability of the solution?
I would rate the scalability as eight.
How are customer service and support?
I would rate technical support from Elastic Search as three out of ten.
The main issue is a general sum of all factors. Being based in Hong Kong means I can only assess the service in my region and cannot speak for other regions based on my experience.
Which solution did I use previously and why did I switch?
I am currently working with multiple solutions including Elastic Search, Splunk, and Graylog.
How was the initial setup?
The initial setup for Elastic Search is complex.
What other advice do I have?
The real-time analytics capabilities depend on whether you use the paid version or open-source version.
I work with SME users of Elastic Search, though the solution can technically support enterprise customers.
I have not extensively used AI technology with Elastic Search.
I can recommend Elastic Search to other users.
The pricing for Elastic Search rates as four out of ten. Overall, I would rate Elastic Search as seven out of ten.
User optimizes data analysis with advanced search features and seeks expanded functionality
What is our primary use case?
I have been using it for a year. The main use cases involved implementing search functionality.
What is most valuable?
When discussing the features of Elastic Search, the full text search capabilities are particularly beneficial for handling large volumes of data.
The full text search capabilities in Elastic Search have proven to be extremely valuable for our operations.
Regarding AI integration, we have not yet implemented any AI-driven projects or initiatives using Elastic Search.
What needs improvement?
There are some features and functionality that could be enhanced in Elastic Search to improve its overall capabilities.
For how long have I used the solution?
I have been using Elastic Search for a year.
What do I think about the stability of the solution?
In terms of performance and stability, Elastic Search has proven to be a reliable solution.
What do I think about the scalability of the solution?
The environment includes multiple users utilizing Elastic Search across different locations.
Which solution did I use previously and why did I switch?
Before implementing Elastic Search, I had experience working with other search engines from different vendors.
How was the initial setup?
The implementation strategy involved specific steps during the setup process to ensure proper configuration.
What was our ROI?
The main benefits observed from using Elastic Search include improvements in operational efficiency, along with cost, time, and resource savings.
What other advice do I have?
I previously used Graylog.
I am currently working with Elastic Search as the primary solution.
My role is Senior DevOps engineer at UVIK Digital.
On a scale of 1 to 10, with 10 being the highest, I would rate Elastic Search as an 8 overall as a product and solution.
Efficient data storage and quick searching boost productivity
What is our primary use case?
Our primary use case was primarily for data storage and quick searching. We focused on getting objects from the database and filtering them efficiently. This involved getting and searching through objects.
How has it helped my organization?
Our productivity was consistently maintained while using this database. Its consistent performance allowed us to maintain steady productivity levels.
What is most valuable?
The most valuable feature of Elasticsearch was the quick search capability, allowing us to search by any criteria needed. The searches were executed very quickly, which made the process reliable. Additionally, full-text queries were integral to our usage. Our productivity was consistently maintained with this database. Its consistent performance allowed us to maintain steady productivity levels.
What needs improvement?
It would be useful if a feature for renaming indices could be added without affecting the performance of other features. However, overall, the consistency and stability of Elasticsearch are already commendable, and they should keep up the good work.
For how long have I used the solution?
I have been using Elasticsearch for two and a half years while at this company.
What do I think about the stability of the solution?
The stability of Elasticsearch was very high, and I would rate it a ten. It was consistent and reliable in our usage.
What do I think about the scalability of the solution?
Elasticsearch was decently scalable, matching our data growth. I would rate its scalability a ten.
How was the initial setup?
I was not involved in the initial setup. However, the setup process for smaller projects was straightforward.
What about the implementation team?
One person from our DevOps team was responsible for the maintenance of Elasticsearch.
What's my experience with pricing, setup cost, and licensing?
We used the open-source version of Elasticsearch, which was free.
What other advice do I have?
If a feature for renaming indices could be added without affecting the performance of all other features, it would be nice to have. Overall, I rate Elasticsearch a ten out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
The command-based configuration simplifies data management and setup
What is our primary use case?
I have used the Wazuh SIEM tool, an open-source SIEM tool that uses Elasticsearch for indexing. In this SIEM tool, we have a large amount of logs. Data are converted into alerts, then they are stored in our environment for monitoring and security purposes. For storing that data in Wazuh, we use Elasticsearch indexing.
What is most valuable?
Configuring Elasticsearch is much easier compared to comprehending other SIEM tools like Splunk. It has a full command-based access that allows you to configure how much data you want to store and set up retention policies. I can easily change the bandwidth for the network to send log data. Elasticsearch is quite user-friendly and offers a hands-on experience for configuring databases.
What needs improvement?
Elasticsearch should have simpler commands for window filtering. It is primarily based on Unix or Linux-based operating systems and cannot be easily configured in Windows systems. Multi-operating system support would be a great improvement.
For how long have I used the solution?
I have used it for approximately two years.
What was my experience with deployment of the solution?
It can be installed on cloud and locally, with no issues.
What do I think about the stability of the solution?
I would rate the stability of Elasticsearch as a seven. There have been multiple instances where I faced errors due to network bandwidth issues. The data transfer sometimes exceeded the bandwidth limits without proper notification, which caused issues.
What do I think about the scalability of the solution?
I would rate the scalability of Elasticsearch as an eight. The high scalability is somewhat limited by its lack of support for different operating systems other than Linux.
How are customer service and support?
I have never used their technical support. I usually resolve issues on my own or with the help of online community forums.
How was the initial setup?
The complexity of the initial setup depends on the requirements. In an MSSP scenario, where multiple clients use the same software, there is a need to segregate the data. This can make the setup more complex, especially for a single client where you need to adjust network configurations.
What was our ROI?
For time-saving, Elasticsearch is a good software. It is stable, and we do not encounter critical issues like server downtime, which could result in data loss. There are minor misconfigurations regarding data transfer rates that I have noticed sometimes.
What's my experience with pricing, setup cost, and licensing?
I'm not familiar with the pricing details as it falls under the finance department. My manager handles the costing. However, given that we have been using it for two years, I can suggest that it's priced sensibly for us.
Which other solutions did I evaluate?
If you can't afford a large SIEM tool like Splunk and QRadar, Elasticsearch is a viable alternative.
What other advice do I have?
Overall, considering key aspects like cost, learning curve, and data indexing architecture, Elasticsearch is a very good tool. I would rate it as a nine.
Improved performance in data aggregation and has a fast performance
What is our primary use case?
I use the solution to store historical data and logs to find anomalies within the logs. That is about it. I don't create dashboards from it.
What is most valuable?
I find the solution to be fast. Aggregation is faster than querying directly from a database, like Postgres or Vertica. It's much faster if I want to do aggregation. These features allow me to store logs and find anomalies effectively.
What needs improvement?
I found an issue with Elasticsearch in terms of aggregation. They are good, yet the rules written for this are not really good.
There is a maximum of 10,000 entries, so the limitation means that if I wanted to analyze certain IP addresses more than 10,000 times, I wouldn't be able to dump or print that information. I need to use paging or something similar as a workaround. That's what the limitation is all about.
For how long have I used the solution?
I have probably used it for three or four years, maybe longer.
What do I think about the stability of the solution?
The solution is very good with no issues or glitches.
What do I think about the scalability of the solution?
In terms of scalability, I have multiple Search instances. I can actually add more storage and memory because I host it in the cloud. It's much easier in terms of scalability, and I have no complaints about it.
How are customer service and support?
I have never talked to technical support.
Which solution did I use previously and why did I switch?
I am using Elasticsearch.
How was the initial setup?
The initial setup is very easy.
What about the implementation team?
I did not use any outside assistance.
What's my experience with pricing, setup cost, and licensing?
I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.
Which other solutions did I evaluate?
I am evaluating InfluxDB as well. Timescub is a kind of database.
What other advice do I have?
I would rate Elasticsearch at eight out of ten.
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