ElasticCloud(Elasticsearch, FedRAMP, SaaS Contract) [Private Offer Only]

The main use case for Elastic Search  is mainly for log management.

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.

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.

I have been working with Elastic Search for about two years.

I usually use Elastic Search on-premises, which introduces complexity in deployment. Using the cloud version would reduce the complexity of setting up.

I would rate the stability for Elastic Search as eight out of ten.

I would rate the scalability as eight.

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.

Negative

I am currently working with multiple solutions including Elastic Search, Splunk, and Graylog .

The initial setup for Elastic Search is complex.

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.

On-premises

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.

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 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.

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.

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.

I have used it for approximately two years.

It can be installed on cloud and locally, with no issues.

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.

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.

I have never used their technical support. I usually resolve issues on my own or with the help of online community forums.

Positive

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.

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.

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.

If you can't afford a large SIEM tool like Splunk and QRadar, Elasticsearch is a viable alternative.

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.

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