Elastic Search Reviews

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.

Public Cloud

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.

On-premises

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. 

Elastic has a lot of products. The one I'm most familiar with is Elastic Observability. It's designed to monitor our applications within an organization. It gives managers visibility into the activity and functionality of applications within the network. I've worked with it both on-premises and in the cloud. It helps us monitor applications and identify any issues. For example, we can see if an application is calling on a database if there are any delays or errors, and what might be causing those problems. It can also give us a proper view of the number of transactions done on the database and other information. It's not just pulling data for us; it's giving us real-time insights into the activities and functionalities of our applications within our network environment.

When users understand the root cause of the problem, they spend less time resolving it. The number one benefit is end-to-end stability. It provides deep visibility into your cloud and distributed applications, from microservices to serverless architectures. It quickly identifies and resolves the root causes of issues, like gaining visibility into all your cloud-based and on-prem applications. It also simplifies issue resolution, leading to faster resolution times and optimized performance. It is achieved through numerous tools, metrics, and application performance fine-tuning systems, ensuring a smooth user experience. That's why many enterprises seek this kind of solution. It provides valuable insights into potential security vulnerabilities, enabling pre-emptive measures and safeguards for your data assets. Then there's data-driven decision-making, which is very important! It breaks down data silos by ingesting all the telemetry data (metrics, logs, etc.) into a single, scalable platform with a contextual data model. This flexibility allows you to collect and visualize any data from any source. Essentially, it pulls data from all sources and guides you in making data-driven decisions for capacity planning, resource allocation, and risk mitigation. Finally, it also fosters collaboration across IT teams.

There are potential improvements based on our client feedback, like unifying the licensing cost structure, which might be helpful for clients. This room for improvement is from my perspective as a salesperson. Because when I give customers the pricing information, they might wonder why there are two different licensing models, unlike competitors like BeyondTrust or Delinea. Delinea also has the same thing with the code.

I have been with this solution for more than six months.

It's very, very stable. Most times, I go through the demo sites, which allows understanding of functionalities and use cases and all of that. I would rate the stability a nine out of ten.

It is a scalable solution. I would rate the scalability a nine out of ten.

The customer service and support are very nice.

Positive

I have experience with Delinea, ManageEngine, BeyondTrust, IBM and WALLIX. But compared to Elastic, they lack the same level of artificial intelligence capabilities. It's like an all-encompassing package with tons of features. One of those features is the ability to pinpoint the root cause of any problem, whether it's code issues (like it was not written properly), developer errors, or anything else. It goes beyond just surface-level troubleshooting and digs deep to give you the real why. That's what sets it apart from the others. Imagine an application is having some issues. Elastic can tell if it's faulty code, a developer mistake, or anything else. It gives you the true root cause, not just the surface-level symptoms. That's its strength and why it stands out as the industry standard.

The initial setup is not complex to me. I've seen it displayed before in a demo presentation with Jakadaz. The solution is not difficult to use. It's very easy. Even as a non-technical person, I could interact with the application.

The deployment doesn't take long because we have experts who can help. It's available both in the cloud and on-premises, so it depends on the customer's choice.

It is a cost-effective solution. It is not expensive.

I would rate it a nine out of ten for now. It has a lot of features compared to other solutions. Its comprehensiveness and range of features are what make it stand out for application monitoring. I highly recommend it. It's very good because it's efficient, highly scalable, and has high availability. Additionally, cost-effectiveness is crucial in Nigeria due to exchange rates. Organizations need solutions that are affordable, and Elasticsearch fits the bill. I would absolutely recommend it to any organization.

Hybrid Cloud

We have a distributed login environment. We have logs in databases and some in files. We use the solution to centralize everything. It's good for monitoring. 

The solution is useful for observing logs. The observability is good. 

It's good for collecting various types of logs. The metrics on offer are great.

We also collect logs from VMs, and we can look at the CPU and RAM situation to see what is being used. 

The APM for our ITSM tools is helpful. It provides good visibility.

It is scalable.

The solution offers good stability.

The initial setup is easy.

We'd like to see more integration in the future, especially around service desks or other ITSM tools. 

I've been using the solution for two or three years. 

The solution is stable and reliable. There are no bugs or glitches. It doesn't crash or freeze. 

It is scalable. It's not a problem if you need to expand it. 

We have about 20 people using the solution right now. We're using it in a test environment right now. Once we deploy to production, 300 to 400 people will use it. 

We have never used technical support. 

Our help desk also uses Grafana. We'll use this solution more widely eventually. 

The initial setup is very easy.

We took about a month to deploy the solution. 

We might need about ten people to handle the deployment and maintenance. We're still in the test environment right now. 

We handled the setup ourselves. We did not need outside assistance. 

I'm not sure of the exact licensing costs. I don't deal with that aspect of the solution. 

I'm using the latest version of the solution. I started with version 7.1, and now I use 8.6.

I'd recommend the solution to other users. 

I'd rate the solution nine out of ten. The features and tools are overall very good. 

On-premises

We use the solution mainly for logs today. There are other teams that use it for other use cases. We just use it for logging and logging search and these kinds of things.

The search capabilities are the best that we could find. It's great for searching for any text with wild cards inside the logs. It's very good. We have a very good performance, even with billions of registries.

The solution is stable and reliable. 

We have an issue with the volume of data that we can handle. When we have a lot of data, like 30 days of logs, the product becomes slow, and we had to reduce it to seven days. Now, we have only seven days of logging.

Logging and tracing are different and we have a problem when it comes to tracing things. If we could have some feature related to tracing between microservices or between any sort of logging, that would be nice.

We've been using the solution for three or four years. We've used it since 2019.

This is a very stable solution. It's reliable. There are no bugs or glitches. It doesn't crash or freeze.

It's scalable in the sense of pods or quantity or numbers of requests, yet not so scalable when considering persistence. We can't handle too much long-term data.

We have at least 500 people using the solution right now.

As a bank, we have some constraints around using and adding new tools. It's very difficult to change stacks. Therefore, we have no plans to stop using the solution anytime soon. 

I've never directly worked with technical support. We have our own support comprised of our own employees. I do not deal with external support services.

We did no previously use a different solution. 

I can't speak to the initial setup. The infrastructure team handled the setup. I did not implement it directly.

It is my understanding we needed three or four engineers to handle the deployment and maintenance process. 

I do not have any details about the cost or licensing. That said, the cost is public, and likely, someone can search for the approximate costs online. 

We are a customer.

I'm not sure which version we're using. I'm from the development team. The people who are doing the configuring work would know the version.

I'd rate the solution seven out of ten. It is a good solution, yet not quite perfect.

We use the solution to monitor our website and APIs request and response cycle, also for log aggregation. We also used it for APM and searching for slow and database queries. 

It helped a lot in identifying bottlenecks and events happening simultaneously among several services, since we can aggregate the logs into a single repository of data

I am impressed with the product's Logstash. The tool is fast and customizable. You can build beautiful dashboards Kibana using Logstash as data source. It is useful and reliable. 

It was not possible to use authentication three years back. You needed to buy the product's services for authentication. 

I have been working with the product for three years. 

The tool itself is stable but depends on your infrastructure. If you have slow disks, the searches tend to take more time. If you need more data retention, be sure to keep an eye on disk space. Otherwise, the service crashes easily.

The tool's scalability is tied to your infrastructure. You need to have the money and resources to scale your infrastructure. To scale up, you need faster disks and more servers.  My company had 15 users using the product for a small API, and the cost was not so high.

The product's tech support is very helpful and skilled.

Positive

The product's setup is difficult, since you need at least 5 servers in a distributed topology to achieve its full potential: 3 machines for elasticsearch, 1 for logstash and another for kibana

In house

"The tool is an open-source product, but you have to self-host it and you need specialized personnel to maintain it.

If you are self hosting the solution, you need to take care of indexes and understand cluster sharding and distributed systems' election system

On-premises