Elastic Search Reviews

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.

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.

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.

I have probably used it for three or four years, maybe longer.

The solution is very good with no issues or glitches.

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.

I have never talked to technical support.

Neutral

I am using Elasticsearch.

The initial setup is very easy.

I did not use any outside assistance.

I don't know about pricing. That is dealt with by the sales team and our account team. I was not involved with that.

I am evaluating InfluxDB as well. Timescub is a kind of database.

I would rate Elasticsearch at eight out of ten.

Public Cloud

Other

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.

Our productivity was consistently maintained while using this database. Its consistent performance allowed us to maintain steady productivity levels.

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.

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.

I have been using Elasticsearch for two and a half years while at this company.

The stability of Elasticsearch was very high, and I would rate it a ten. It was consistent and reliable in our usage.

Elasticsearch was decently scalable, matching our data growth. I would rate its scalability a ten.

I was not involved in the initial setup. However, the setup process for smaller projects was straightforward.

One person from our DevOps team was responsible for the maintenance of Elasticsearch.

We used the open-source version of Elasticsearch, which was free.

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.

Public Cloud

Amazon Web Services (AWS)

I have been using it for a year. The main use cases involved implementing search functionality.

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.

There are some features and functionality that could be enhanced in Elastic Search to improve its overall capabilities.

I have been using Elastic Search for a year.

In terms of performance and stability, Elastic Search has proven to be a reliable solution.

The environment includes multiple users utilizing Elastic Search across different locations.

Before implementing Elastic Search, I had experience working with other search engines from different vendors.

The implementation strategy involved specific steps during the setup process to ensure proper configuration.

The main benefits observed from using Elastic Search include improvements in operational efficiency, along with cost, time, and resource savings.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Private Cloud

Other

It is basically for the banking and non-banking sectors. We use it for the APM perspective and application performance monitoring, but not in a holistic way; it is just layer seven, layer five, and six that are there.

In analytics, people use it for search patterns. I've also used Elasticsearch for indexing, where we can have content and do these things. But from an analytics perspective, I have never used Elasticsearch. I have used it in one project

It's a good tool because if you compare it with MongoDB, MongoDB is better. It has a very good data warehouse and search pattern. Elasticsearch cannot be made into a data warehouse. You can use it for smaller-scale analytics, but if you are looking at anything over 30-40 TB, it's not a data lake or big data solution. 

It's a normal database, and any Oracle database or enterprise DB like MSSQL or PostgreSQL can do these things. I've never used it for unstructured data. I have used MongoDB, but not for this.  

All features are almost the same as other observability tools. The best part I like is that it becomes a MOM aka monitoring of monitors. It can capture data from all other sources. It's not a unique feature of Elasticsearch itself because other tools like Dynatrace do do the same thing. But from an ROI perspective and a user-friendly perspective, it is a good tool.

Even at level four to level seven of the OSI model, it does monitoring very well. There are a lot of AI-embedded tools or prediction tools, and numerous default reports are available, which get populated easily. 

So, the quality features are there. There are about 60  to 70 odd reports available. When you deploy the tool and the logs come in, they can capture those logs and automate field mapping and other things. That's the feature—by default, a few reports are available.

The data indexing capability of Elasticsearch is very good. It does the indexing correctly. It's not over-indexing, so it's perfect. It's very good. But how it works depends on the customization of the application and the search pattern you want. The log can be easily viewed, and based on that, you can easily tag things.

Scalability and ROI are the areas they have to improve. Their license terms are based on the number of cores. If you increase the number of cores, it becomes very difficult to manage at a large scale. For example, if I have a $3 million project, I won't sell it because if we're dealing with a 10 TB or 50 TB system, there are a lot of systems and applications to monitor, and I have to make an MOM (Mean of Max) for everything. This is because of the cost impact. 

Also, when you have horizontal scaling, it's like a multi-story building with only one elevator. You have to run around, and it's not efficient. Even the smallest task becomes difficult. That's the problem with horizontal scaling. They need to improve this because if they increase the cores and adjust the licensing accordingly, it would make more sense.

I have been using it for more than four to five years. 

I would rate the stability a nine out of ten. It is a good product. It is a stable product. 

Elasticsearch has horizontal scalability. The users can scale up to any level. The only problem is related to disaster recovery. After some time, it becomes very difficult to do the DC/DR mapping because observability is a critical tool for event alerts. It becomes difficult to manage real-time events if the primary data center goes down and the disaster recovery site needs to take over. This is an issue for large projects like those at tier-one organizations like Ford or big banks. For mid-level and lower-level tier-two or tier-three organizations, it is good.

Another thing to consider is that Elasticsearch has high resource utilization on both the vertical and horizontal levels. But it's a good product for tier-two organizations.

All my clients are enterprise businesses. 

I've never heard anything wrong from the delivery side, but it's an international company with a very good product. So, the support system should be good.

Positive

I tried to sell Kibana twice, but in terms of deployment, we've used it in two or three places. However, I don't have hands-on experience with Kibana. 

To be very honest, we faced some setbacks with Kibana, particularly with network-level monitoring. This issue occurred a few weeks ago when I tried to sell one of our products. We have used Kibana for APM purposes, as well as the Elasticsearch ELK stack.

From an application perspective, it’s one of the tools we use. I can share a lot of insights, but I haven't seen all their reports or dashboards. So, my experience is from a presales perspective rather than a deployment perspective.

If I compare it with other auxiliary tools like Dynatrace, SolarWinds, or Relay, Elasticsearch is very competitive and user-friendly. 

One thing about Elasticsearch is the way they sell licenses for their database, which can be a bit hidden. Many people think Elasticsearch is entirely open-source, but there are charges involved. It's an MPP-based NoSQL database with some limitations on certain datasets.

I would rate my experience with the initial setup a nine out of ten, with ten being easy. It is easy, not that difficult. 

It can be deployed both on the cloud and on-premises. I've seen on-premises deployments. This is especially true in other parts of the world where governments don't want to use the private cloud and have their own private cloud. I have mostly worked with on-premises deployments.

The mapping can take three months on average. However, the deployment time depends on the project. If you have a hundred servers, it will take two or three weeks. With three or four thousand servers, it will take longer. It's the same with any tool, like Dynatrace or SolarWinds. We have to map services and events, set thresholds, and configure event triggering and notifications. There's a lot to consider, so it depends on the project scope, the number of servers, the data captured, and whether it's agent or agentless. It's difficult to calculate an average about how many days it will take.

I would rate the pricing an eight out of ten, with one being cheap and ten being expensive. It is not very costly, but it is not cheap either. 

I would rate it to others. Elasticsearch can be used for many things. It has a good indexing parameter and can be used for search patterns and more. 

If it's for observability, I would give it a nine out of ten. The only issue I have is with APM (Application Performance Monitoring).

Elasticsearch as a product is different than Elasticsearch as a search engine. Elasticsearch is also different as an analytics tool. It depends on the analytical solution and how they want to fetch data from Elasticsearch as a database. As a search engine, it is one of the best. 90% of people use either Solar or Elasticsearch for web portals and other things. Nobody can challenge Elasticsearch in that area. So, out of ten, I would give it a ten.

But for analytics, I'd give it an eight. It depends on my database and in-memory tools. If I use QlikView or other tools, I'll just use Elasticsearch as a database. It's just like any other database they are using for in-memory analytics. 

For observability, Elasticsearch, Logstash, and other things, it is a good component. It's good for tier-two enterprises. But when you define "enterprise," you must be specific. If you mean more than 2000 servers, then 90% of people won't consider it. There are other observability tools on the market. So, be specific in your query.

I can describe a project where we use Elasticsearch, Logstash, and Kibana (ELK stack) for our archiving objectives. I work in the security department of a Fintech company in the payment industry. We use the ELK stack to connect our internal systems with the bank's systems and we used Beats for data collection. We then store and forward this data to Elasticsearch for indexing and analysis, visualize and create alerts using Kibana based on categorized access logs, identifying and blocking malicious traffic or payloads.

Logsign provides us with the capability to execute multiple queries according to our requirements. The indexing is very high, making it effective for storing and retrieving logs. The real-time analytics with Elastic benefits us due to the huge traffic volume in our organization, which reaches up to 60,000 requests per second. With logs of approximately 25 GB per day, manually analyzing traffic behavior, payloads, headers, user agents, and other details is impractical.

I don't see improvements at the moment. The current setup is working well for me, and I'm satisfied with it. Integrating with different platforms is also fine, and I'm not recommending any changes or enhancements right now.

I have been using Elastic Search for the past year.

We subscribed to NGINX for technical support, and they were helpful during the installation phase. There is a lack of community support for GRPC, which needs improvement. 

we are using a licensed version of the product. 

We are fully satisfied with the usage and support, rating it 8 out of 10. I recommend NGINX for managing traffic due to its multiple functionalities like load balancing, proxy management, and caching.

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

Elastic Search significantly improved our blog management at the organization. It played a crucial role in enhancing our cybersecurity efforts by allowing me to identify the geographical origins of web traffic. This feature was instrumental in identifying and mitigating potential threats from various actors. The ability to pinpoint the source of the traffic helped in quickly addressing security concerns and filtering out potential threats.

The most valuable features are its user-friendly interface and seamless navigation. The abundance of tutorials and helpful mocktails significantly contributed to the ease of managing the system. The user interface stood out for its accessibility, making it straightforward to perform tasks and queries. The availability of resources, such as tutorials and mocktails, not only facilitated my learning process but also enhanced the overall usability of Elastic Search. Additionally, the ability to seamlessly integrate Elastic agents into our system not only enhanced our overall efficiency but also facilitated smooth integration with the cloud. The versatility of adding Elastic agents and leveraging the source components provided a comprehensive solution for managing and optimizing our system.

Elastic Search could benefit from a more user-friendly onboarding process for beginners. Creating a module or series specifically designed for those new to Elastic Search would be valuable, starting with the basics and gradually introducing the integration of Elastic Search with emerging technologies like AI. Additionally, it would be helpful to see improvements in mailing integration and potentially offer a more accessible pricing tier for individuals or students who are just starting to explore security and monitoring aspects. A tier tailored for the average user, focusing on simplicity and affordability, could attract a broader audience and encourage long-term use.

I would rate the stability of Elastic Search at a solid nine out of ten. Throughout my usage, I didn't encounter any failures, errors, or unexpected shutdowns. It was a reliable experience, and the fact that it didn't stop working unexpectedly was a relief.

It is quite scalable.

I used a different solution before switching to Elastic Search because Elastic Search offered a wider range of features. The other solution focused on monitoring app usage, Elastic Search stood out with its extensive modules, cloud deployment options, and flexible monitoring capabilities. Despite Splunk being a bigger name, I found Elastic Search to be more versatile and enjoyed using it.

Initially, deploying Elastic Search was a bit challenging for me as it was my first time using the Elastic Stack. Setting it up to monitor traffic on multiple VMs, including an enterprise-level VM, posed some configuration hiccups, especially when connecting to the cloud. I had to rely on online resources and Google searches to troubleshoot and figure things out. While I eventually got through it, the process felt overwhelming with lots of information to digest at once. As for maintenance, during the short period I used it for a lab, there wasn't much to handle beyond shutting it down and reinstalling it at the end. I can't speak to long-term maintenance since my usage was relatively brief.

Elastic Search has provided a valuable return on investment by enhancing effectiveness and aiding in learning about security features. It has saved me an estimated couple of hundred dollars in both time and money.

Elastic Search is a bit pricey, especially for individuals or small learners interested in cybersecurity. It could be more affordable for personal use, making it accessible to a broader audience learning about network security and traffic monitoring.

My advice to anyone who is evaluating Elastic Search is to explore the user-friendly website and navigate to the documentation or resources section. Start with a basic overview of the components, and how they work together, and try simple tasks like searching or detecting. The key is to begin with something straightforward. Utilize the documentation to understand how to get started and explore the various integrations Elastic Search offers. Overall, I would rate it as an eight out of ten.

Public Cloud