Elastic Search Reviews

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.

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.

The primary use case for Elasticsearch is to serve as a non-SQL database platform to replace traditional SQL processes. It is used in situations where unstructured data needs to be studied and searched.

Elasticsearch has been helpful due to its ability to handle unstructured data effectively compared to SQL. It provides a fast and interesting search capability which is advantageous for our needs.

The most valuable feature of Elasticsearch is its convenience in handling unstructured data, making it easy to use.

Elasticsearch could be improved in terms of scalability. If the database becomes too large, its efficiency is not as good as SQL. Additionally, the initial setup could be a little easier.

We have been using Elasticsearch for about two to three years.

We have faced shutdown issues, but these are mostly related to problems with our own machines and not due to Elasticsearch itself.

Elasticsearch is not scalable when dealing with very large databases. The efficiency decreases for huge databases because it deals with unstructured data, which presents an inherent problem.

The initial setup is of medium difficulty since it requires some understanding of the disk and related concepts.

Elasticsearch can be expensive. It requires some support and unlocking of features.

I recommend Elasticsearch for anyone looking to build a simple database, as it should be a top choice.

I'd rate the solution seven out of ten.

For me, the primary use case of Elasticsearch is log analysis, as it is a text-based search tool. To explain how it works, let's consider its role at the backend. Elasticsearch operates on keywords used to fetch data. This is in contrast to some databases, where operations might be based on a key order or a primary key, allowing for various maintenance and analysis tasks.

Many people use Elasticsearch to store their application logs in JSON format. These logs are indexed, facilitating efficient search and analysis. Additionally, Elasticsearch integrates well with tools like Grafana and Kibana, enabling users to create diverse dashboards for data visualization.

There's also the text-based search scenario. For instance, if a user wants to search for something using a specific keyword, Elasticsearch excels in this area by creating multiple indices. 

Elasticsearch is a versatile tool that can store and retrieve information effectively, making it suitable for various applications across different industries.

Elasticsearch is a quick search engine tool. A good use case is saving metadata of your systems for data cataloging. Various systems, like those opened in metadata and similar applications, use Elasticsearch to store their text data. However, the major use case for many is to store application logs and build different dashboards on top of it.

The use of Elasticsearch is very specific. It is not helpful for storing your OLTP data. Elasticsearch's specific use is when you need to provide text-based search functionality. That's when Elasticsearch becomes relevant. 

For instance, for log analysis or searching values, Elasticsearch performs very well. However, there are challenges with performance management and scalability, particularly how developers manage these aspects. 

For example, Kubernetes is a popular choice as it offers the needed features to run your application and allows performance optimization in response to increased system load, and managing itself. If you plan to deploy Elasticsearch with limited or predefined resources, it may not be the ideal setup. 

Therefore, it's better to create ultimate commerce capabilities for it. This is the challenge people are facing in the market and the solution for it. So, this answer combines two aspects: the challenge and its solution.

I have been using Elasticsearch for almost a year now. I'm comfortable working with it and understand its functionalities. 

In our organization, it's not so much about the number of people as it is about the number of products utilizing it. Currently, we use Elasticsearch in more than 12 products. 

It's become essential for any component that requires text-based functionality. Besides that, it's also used for logging to analyze application performance, peak times, etc. Elasticsearch is a basic component of the architecture for each of these products.

Most of our deployments are not exposed to the Internet or public networks; they're restricted to closed networks. We don’t frequently upgrade from previous versions unless a specific use case arises. 

In such cases, we usually turn to the developer community for support. 

Another scenario is when running the application in a careful mode, where the main requirement is to change the image name in the configuration. Then, we check for any changes or incompatibilities with previous versions. Upgrades can sometimes introduce issues if they’re not compatible with existing configuration files, but it's generally not too problematic to handle.

Deploying in Kubernetes is not complex. There are many resources in the market, like DevOps guys and guides, which make the process straightforward. The deployment can be done in a matter of minutes. You basically run a configuration file to set up your application, define replicas, and so on. It shouldn't take much time; even with an expert, it's a matter of a few hours. 

However, the key lies in following best practices and configuring your files properly. If you follow the best practices, you'll likely face fewer issues. But if not, problems are inevitable. 

It’s crucial to analyze these practices, considering factors like bandwidth, data volume, user interaction, and how it's read by different applications. These considerations help in managing resources and scalability, including scaling up and down your Elasticsearch container. These points are vital for running Elasticsearch efficiently, especially for text-based search applications.

You can deploy it as required. Elasticsearch is versatile; you can run it on Kubernetes, in the cloud, or on-premises. There is no limitation in terms of deployment options.

The cost varies based on factors like usage volume, network load, data storage size, and service utilization. If your usage isn't too extensive, the cost will be lower. 

However, if you're dealing with high volumes, you'll need to reconsider the cost-effectiveness. If there are no challenges or bottlenecks in buying a service from a cloud service provider, that might be a viable option. 

But if you're concerned about price or issues like exposing your data to the public cloud, then deploying on-premises and conducting stress testing becomes important. It’s a part of the learning and development process, not just a deployment for production. 

You need to pass through testing processes in the development environment and then move to staging and production. This involves various tests to understand user access patterns, data push, and performance assessment. Deploying on your own requires considering all these factors. On the other hand, if you use a cloud service, many of these concerns aren't your responsibility.

If you're interested in using Elasticsearch as a search tool and for cloud data integration, comparing it with alternatives like Amazon Cloud Search or Azure Search is valid. Many cloud service providers that offer text-search services are utilizing Elasticsearch. They've implemented best practices and resolved a myriad of issues experienced by companies using Azure, AWS, or GCP. 

These providers have integrated Elasticsearch into their cloud offerings effectively. Choosing their services might be preferable due to lower operational costs on your side. 

In case of any disaster or issue, their development and DevOps teams are available to support you. However, if you face limitations, like client requirements prohibiting data storage in public or private clouds, then deploying Elasticsearch on-premises would be your alternative.

I would definitely rate it an eight out of ten,  which is very good. The reason is the active community continuously working on it, and the support from contributors and the support team is notable. Because Elasticsearch is very specific in its use cases. 

It excels in text-based search and creating dashboards for application logs. It provides results and functionality that are hard to find in alternative tools. So, if you have a use case that fits, Elasticsearch is a great service without any direct alternatives.

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.

Positive

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 Elastic Enterprise Search to develop robust and competitive projects. 

We provide clients with a chance to upload a file. They have all the accounts and transactions that they want to do and we have a limit of 10,000. In two minutes, they can have the 10,000 transactions in their accounts.

I would like to see Elastic Enterprise Search focus on interbank transfers, maybe in another way of payments. We have a feature focused on online interbank transfers. But, it would be good to see current payments for workers. They could also provide this same product to the retail clients. The price support could be improved as well. 

We have been using Elastic Enterprise Search for about a year and a half.

Elastic Enterprise Search is stable. On a scale of one to 10, with one being not very stable and 10 being very stable, I give Elastic Enterprise Search a nine. 

Elastic Enterprise Search is scalable. On a scale of one to 10, with one being not scalable and 10 being very scalable, I give Elastic Enterprise Search a 10. 

Elastic Enterprise Search's tech support is good but it could be improved. 

Positive

The initial setup is difficult. On a scale of one to 10, with one being very difficult and 10 being very easy, I would rate Elastic Enterprise Search a two. 

We are continuously integrating new features. Everything we added this month took about a day each to deploy. We have 22 software engineers on the project, along with two tech leads and one solution architect.

The price we pay for Elastic Enterprise Search is very high. We have a complicated banking project with a lot of components, developers, and features. 

On a scale of one to 10, with one being very cheap and 10 being very expensive, I would rate this solution an nine. Their pricing system is highly complex. 

It has helped by notifying me when something happens. I deploy my team to the infrastructure to fix the application. However, receiving alerts before something happens would be more beneficial.

New Relic is very similar to Elasticsearch in functionality; it's easier to use.

New Relic could be more flexible, similar to Elasticsearch. It could improve on providing notifications before something happens instead of when something happens.

It is a stable and good platform.

It's scalable. There's no need to worry about the environment. You just configure it, and it runs without issues.

I haven't used their support, however, a colleague I talked to about this platform with has used it.

Positive

The initial setup is not complex. The only part that may require specific knowledge is communicating your cloud environment with New Relic and managing the cloud environment configurations.

Comparing the costs between New Relic and Elasticsearch is difficult as New Relic's cost is for processing metrics, whereas Elasticsearch's cost is for storage.

I recommend New Relic, however, it depends on the specific use case you have. I'd rate the solution eight out of ten.