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Elastic Enterprise Search OverviewUNIXBusinessApplication

Elastic Enterprise Search is #1 ranked solution in top Indexing and Search tools and top Search as a Service vendors. PeerSpot users give Elastic Enterprise Search an average rating of 8.2 out of 10. Elastic Enterprise Search is most commonly compared to Azure Search: Elastic Enterprise Search vs Azure Search. Elastic Enterprise Search is popular among the large enterprise segment, accounting for 66% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 20% of all views.
Elastic Enterprise Search Buyer's Guide

Download the Elastic Enterprise Search Buyer's Guide including reviews and more. Updated: September 2022

What is Elastic Enterprise Search?

Elastic Enterprise Search (Previously known as Swiftype) is Elasticsearch, with a complete set of specialized tools and extensible APIs that make it easy to build search solutions and give users the best answers, every time. Monitor performance with robust analytics, tweak relevance in real-time, and scale it all seamlessly.

Elastic Enterprise Search Customers

HotelTonight, Perceivant, Docker, Green Man Gaming, Xoom, AutoScout24, TheLadders, Center for Open Science, Parleys, Tango

Elastic Enterprise Search Video

Elastic Enterprise Search Pricing Advice

What users are saying about Elastic Enterprise Search pricing:
  • "The basic license is free, but it comes with a lot of features that aren't free. With a gold license, we get active directory integration. With a platinum license, we get alerting."
  • "We are using the open-sourced version."
  • "We are using the Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition."
  • "The solution is not expensive because users have the option of choosing the managed or the subscription model."
  • "It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process."
  • Elastic Enterprise Search Reviews

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    Kiran BM - PeerSpot reviewer
    Chief Data Scientist at Everlytics Data Science Pte Ltd
    Real User
    Top 10
    The go-to stack for machine- and sensor-generated data use cases. Easy to deploy and maintain. Elastic's ELK Elasticsearch, unlike AWS Elasticsearch, comes with batteries included.
    Pros and Cons
    • "ELK Elasticsearch is 100% scalable as scalability is built into the design"
    • "The metadata gets stored along with indexes and isn't queryable."

    What is our primary use case?

    I'm involved in architecting and implementing Elasticsearch-based solutions, catering to various use cases including IIoT, cybersecurity, IT Ops, and general logging and monitoring.

    The intention of this article is not to compare AWS Elasticsearch with Elastic ELK Elasticsearch and at the end declare the winner. Elasticsearch by itself is one of the coolest and versatile Big Data stacks out there. If you are planning to use it in your organization or trying to evaluate if it is the right stack for your product/ solution, this article offers some insights from an architect's perspective.

    How has it helped my organization?

    I'm not the right person to answer this question as I'm the service provider. My clients are the right people to answer.

    What is most valuable?

    The Spaces feature in Kibana is really useful. I can ingest all data and then offer multi-tenancy on a single stack to various departments (internal) or customers (external). This feature isn't available in AWS Elasticsearch, and Machine Learning isn't available either.

    Other useful features such as Canvas (used to create live infographics) and Lens (used to explore and create visualisations using a drag-and-drop feature) are available only in Elastic's ELK Elasticsearch.

    In the last 18 months Elastic has really caught up and also gone way beyond AWS by putting together all the missing components that make ELK Elasticsearch the most comprehensive stack in the entire Big Data ecosystem. Comprehensive because one stack addresses all of the three essential technical components of an end-to-end system: collect, store and visualise terabytes (and even petabytes) of structured or semi-structured data at ease.

    What needs improvement?

    Enhance the Spaces feature to make it fully multi-tenant by enabling role-based access control (RBAC) at a Space level rather than overall Kibana or stack level like it is currently.

    Elastic needs to work on their Machine Learning offering because currently they have been trying to make it a black box which doesn't work for a serious user (a Data Scientist) as it doesn't give any control over the underlying algorithm. It's like a point-and-click camera vs a DSLR. The offering started with a single/ univariate anomaly detection on time-series data. Now, they have a multivariate which is good, but beyond this, we cannot build any other Machine Learning models, like traditional supervised models. Anomaly detection uses mostly unsupervised algorithms and also it is a very broad problem space for a black box to solve it fully.

    Make index’s metadata searchable (or referenceable in search queries).

    Buyer's Guide
    Elastic Enterprise Search
    September 2022
    Learn what your peers think about Elastic Enterprise Search. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    633,572 professionals have used our research since 2012.

    For how long have I used the solution?

    5 years

    What do I think about the stability of the solution?

    Elastic ELK Elasticsearch is one of the most stable Big Data engines and the simplest to maintain and scale. Redundancy is built into the design so there is no single point of failure. We can configure a DR easily and if something goes wrong, we can restore the system into a brand new cluster in hours.

    What do I think about the scalability of the solution?

    Elasticsearch by itself is 100% scalable as scalability is built into the design like any Big Data system. We just have to add more nodes, and it scales horizontally and then redistributes the data into the new nodes, and the cluster becomes faster and agile automatically. Cross-cluster replication comes with a Platinum license. But this feature is highly exceptional and not a common need.

    Which solution did I use previously and why did I switch?

    I have worked with all the flavours of Elasticsearch viz. Elastic.co's ELK which is popularly known as the ELK stack (pronounced as 'yelk'), AWS Elasticsearch and Open Distro plugins for Elasticsearch.

    All (including Solr that comes with Hadoop) are built on a common underlying technology, Apache Lucene. The difference is the added features that I call 'batteries included'. To be precise, Elastic's ELK Elasticsearch, unlike others, comes with free enterprise-grade apps (called plugins in Kibana) and a bunch of cool and useful Kibana features. It also features a good deal of engineering automation conveniences built into the stack.

    Did you know that the original founders of Elasticsearch are the folks at Elastic.co, the company that has recently transitioned to an open-core philosophy by design. But since AWS took the initial lead and started offering the stack as AWS Elasticsearch service it became more popular and a preferred option for the uninformed. Elastic, on the other hand, was busy innovating and adding more muscle to the stack that it is no more limited to being just the fastest search engine on the planet. In fact, the keyword ‘search’ in Elasticsearch is not relevant anymore and, moreover, it is misleading.

    How was the initial setup?

    Initial setup is indeed straightforward and fast because it will mostly be a single-node cluster. But as the data volume grows and we start seeing a performance lag, the stack requires scaling (by adding more nodes) and a professional intervention for doing the right capacity design and configuration fine tuning.

    What about the implementation team?

    It is always a good idea to engage a professional vendor to implement it right the first time and save yourself a lot of time in experimenting and trying to figure out the optimisation hacks and how-to’s all by yourself.

    What was our ROI?

    A stack like Elasticsearch that enables heavy lifting of the data effortlessly comes with its intrinsic yet obvious ROI. If one is not able to realise the ROI it means either the data is bad (garbage in, garbage out) or the stack is not implemented properly.

    What's my experience with pricing, setup cost, and licensing?

    The basic license is free, and it comes with a lot of features that aren't supposed to be free! With a Gold license, we get Alerting (called Watcher) and some modest enterprise features. Note that if alerting is a must feature for you, you can install open-source alerting plugins like Open Distro Alerting or ElastAlert and avoid the Gold license cost. Active Directory integration, SAML, SSO, Machine Learning etc. come with Platinum license. The licensing is per-node and per-annum basis for an on-premise installation and for Cloud Elastic-managed service the cost is baked into the hourly pay-as-you-go fee. Kibana does not have a license, so it's free.

    If you don't want alerting, Active Directory or LDAP integration and are good with native authentication, the basic license will suffice. The basic license also comes with many internal stack features, which are free. For example, data segregation into hot and warm storage, automatic configuration, and rolling over the index after achieving a certain size limit. 

    SIEM (Security Information and Event Management) app is free. Also is another cool app called Uptime that helps us monitor the uptime of servers and web services. We can do this without any third-party licensing cost. Just turn on the apps, ingest data using Beats and the apps will start thriving. Over time they become mission critical to your business.

    For example, the SIEM app will automatically populate the dashboards and allow us to monitor network traffic, successful logins, unsuccessful login attempts, and anomalous security events. All that comes off the shelf and is free. You'll pay a lot, on the other hand, for a traditional SIEM like ArcSight or LogRhythm.

    Another free app called Infrastructure (formerly known as Metrics) helps monitor the server infrastructure by configuring light-weight data collectors called MetricBeats (for Windows systems) and AuditBeats (for Linux systems). The Beats will start pumping in all the system performance metrics into the stack and help monitor the memory, CPU and disk utilization.

    Which other solutions did I evaluate?

    I have worked with all the flavours of Elasticsearch viz. Elastic.co's ELK which is popularly known as the ELK stack (pronounced as 'yelk'), AWS Elasticsearch and Open Distro plugins for Elasticsearch.

    All (including Solr that comes with Hadoop) are built on a common underlying technology- Apache Lucene. The difference is the added features that I call 'batteries included'. To be precise, Elastic's ELK, unlike the others, comes with free enterprise-grade apps (called plugins in Kibana), a bunch of cool and useful Kibana features, and a good deal of engineering automation built into the stack.

    Moreover, the original founders of Elasticsearch are the folks at Elastic.co, the company that's built on open-core philosophy. But AWS took the initial lead and offered the stack as AWS Elasticsearch service catering mostly to search-engine use cases. But ELK, with all its goodness, is much more than a search engine! In fact, the keyword search in Elasticsearch is very misleading.

    What other advice do I have?

    You can spin up Elastic ELK Elasticsearch fully-managed service either on AWS, GCP, or Azure, or have your own on-premises installation and dockerize it. Whereas the AWS Elasticsearch is available only on AWS. That's the hosting difference.

    Elastic ELK Elasticsearch comes with a support-only subscription, and there are a lot of updates happening. Kibana is constantly improved and there’s a new release every two weeks.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Kiran Raparti - PeerSpot reviewer
    Head of Technology Operations at a financial services firm with 11-50 employees
    Real User
    Top 20
    Open-source with good community support but number of search queries is limited
    Pros and Cons
    • "The most valuable feature is the out of the box Kibana."
    • "I would like to be able to do correlations between multiple indexes."

    What is our primary use case?

    I run the function to review the usage for the team and for the organization itself.

    We use this product internally and then some of our business relationships with the other businesses that we have, they get their data from our data. It's more for collaborative data reporting that we have with them.

    What is most valuable?

    The most valuable feature is the out of the box Kibana. You plug it in and start the basic analysis on the data out of the box. This also gives a quick way to check the data and the models to figure out what fits the needs.

    What needs improvement?

    There are a few things that did not work for us. 

    When doing a search in a bigger setup, with a huge amount of data where there are several things coming in, it has to be on top of the index that we search. 

    There could be a way to do a more distributed kind of search. For example, if I have multiple indexes across my applications and if I want to do a correlation between the searches, it is very difficult. From a usage perspective, this is the primary challenge.

    I would like to be able to do correlations between multiple indexes. There is a limit on the number of indexes that I can query or do. I can do an all-index search, but it's not theoretically okay on practical terms we cannot do that.

    In the next release, I would like to have a correlation between multiple indexes and to be able to save the memory to the disk once we have built the index and it's running.

    Once the system is up, it will start building that in memory.

    We need to be able to distribute it across or save it to have a faster load time.

    We don't make many changes to the data that we are creating, but we would like archived reports and to be able to retrieve those reports to see what is going on. That would be helpful.

    Also, if you provide a customer with a report or some archived queries, that the customer is looking at when they are creating, at first it will be slow while putting up their data or subsequently doing it. I want it to be up and running efficiently. 

    If the memory could be saved and put back into memory as it is, then starts working it would reduce the load time then it will be more efficient from a cost perspective and it will optimize resource usage.

    For how long have I used the solution?

    I have been familiar with this product for approximately four years.

    What do I think about the stability of the solution?

    ELK Elasticsearch is stable.

    What do I think about the scalability of the solution?

    It's scalable, but there are some limitations.

    If you are scaling a bit too quickly, you tend to break the applications into different indexes. 

    The limitations come in when getting the correlation between the applications or the logs.

    It is difficult to get the correlations once the indexes have been split.

    How are customer service and technical support?

    We are using the open-source version, that is installed on-premises.

    We have not worried about technical support, but the community is good.

    Which solution did I use previously and why did I switch?

    Before ELK, we used another solution for internal usage, and also, we used Splunk for different use cases in a different organization altogether.

    It wasn't a switch per se, it was a different organization with a different use case.

    How was the initial setup?

    The initial setup is simple, not too difficult. 

    Getting the index, doing your models, and putting the data in, correctly, is done more on a trial and error basis. You have to start early and plan it well to get it right.

    What's my experience with pricing, setup cost, and licensing?

    We are using the open-source version. 

    We are not looking into the subscription because it's on-premises in-house.

    What other advice do I have?

    For anyone who is looking into implementing this solution, the only tip is to get your models for the type of actual use that you are looking at upfront in order to have a good run.

    I would rate ELK Elasticsearch a seven out of ten.

    Which deployment model are you using for this solution?

    On-premises
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Buyer's Guide
    Elastic Enterprise Search
    September 2022
    Learn what your peers think about Elastic Enterprise Search. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    633,572 professionals have used our research since 2012.
    Murat ERAYDIN - PeerSpot reviewer
    Owner and CEO at Karmasis
    Real User
    Top 20
    Good search speed and easy to deploy, but complicated to scale and needs an ODBC driver and better licensing
    Pros and Cons
    • "The search speed is most valuable and important."
    • "Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one." In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release."

    What is our primary use case?

    We are developing a SIEM application that is similar to QRadar, ArcSight, or Splunk. This application uses Elasticsearch as its search engine because we want to retrieve information fast. We are just using the basic search engine part of Elasticsearch. We have developed lots of things on top of Elasticsearch, such as security, correlation, reporting, etc.

    What is most valuable?

    The search speed is most valuable and important.

    What needs improvement?

    Its licensing needs to be improved. They don't offer a perpetual license. They want to know how many nodes you will be using, and they ask for an annual subscription. Otherwise, they don't give you permission to use it. Our customers are generally military or police departments or customers without connection to the internet. Therefore, this model is not suitable for us. This subscription-based model is not the best for OEM vendors. 

    Another annoying thing about Elasticsearch is its roadmap. We are developing something, and then they say, "Okay. We have removed that feature in this release," and when we are adapting to that release, they say, "Okay. We have removed that one as well." We don't know what they will remove in the next version. They are not looking for backward compatibility from the customers' perspective. They just remove a feature and say, "Okay. We've removed this one."

    In terms of new features, it should have an ODBC driver so that you can search and integrate this product with existing BI tools and reporting tools. Currently, you need to go for third parties, such as CData, in order to achieve this. ODBC driver is the most important feature required. 

    Its Community Edition does not have security features. For example, you cannot authenticate with a username and password. It should have security features. They might have put it in the latest release.

    For how long have I used the solution?

    I have been using this solution since version 1.0.

    What do I think about the scalability of the solution?

    For a one-node installation, it is easy. You can do it and retrieve information fast, but when you are trying to scale up, everything becomes complicated. If you want to deal with several terabytes of data, you should read whitepapers or case studies or get proper consultancy from Elasticsearch. Otherwise, you will lose data. I know many customers who lost their data and could not recover it. It is not like you store everything and search for everything, and it is just instant. It is not like that. You should do your homework very intensively. It looks easy, but when you scale up, it gets complicated.

    How are customer service and technical support?

    We got 60 days of development consultancy with them. Until we sign the agreement, they were quick and prompt. After the signature it changed. Overall experience, we are not satisfied with the development consultancy.

    Which solution did I use previously and why did I switch?

    We switched from SQL Server to Elasticsearch. For our application, we wanted the information very fast without locking everything. In SQL Server or Oracle, that would not have been possible. Deleting is also very difficult in SQL Server.

    How was the initial setup?

    Its initial setup is straightforward. There were no problems.

    What's my experience with pricing, setup cost, and licensing?

    We are using the Community Edition because Elasticsearch's licensing model is not flexible or suitable for us. They ask for an annual subscription. We also got the development consultancy from Elasticsearch for 60 days or something like that, but they were just trying to do the same trick. That's why we didn't purchase it. We are just using the Community Edition.

    Which other solutions did I evaluate?

    We evaluated other products and chose Elasticsearch because the data that we are collecting is unstructured. Every log has a different structure.

    What other advice do I have?

    The most important thing to keep in mind is that it is not as they advertise on their site. If you want to scale up and are looking for a big deployment, you must read everything. You also need support from the company itself. 

    I would rate ELK Elasticsearch a seven out of ten.

    Which deployment model are you using for this solution?

    On-premises
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Uwe Wächter - PeerSpot reviewer
    Senior Consultant at a tech services company with 10,001+ employees
    Real User
    Top 10
    Stable, offers good value for money, and requires very little maintenance
    Pros and Cons
    • "The initial setup is very easy for small environments."
    • "There are a lot of manual steps on the operating system. It could be simplified in the user interface."

    What is our primary use case?

    Our main use case is to centralize all the logs from the infrastructure environment and the data center.

    What is most valuable?

    The most valuable aspect of the solution is the visualization with Kibana. What we have not yet started, yet, we plan to do, is to use machine learning.

    The initial setup is very easy for small environments.

    There is very little maintenance needed.

    The solution is stable.

    The scalability is good.

    The solution offers good value for the price.

    What needs improvement?

    They could simplify the Filebeat and Logstash configuration piece. There are a lot of manual steps on the operating system. It could be simplified in the user interface.

    For how long have I used the solution?

    I've been using the solution for about a year at this point.

    What do I think about the stability of the solution?

    The stability is really good. We use it in a fully virtualized environment, and that's not a real recommendation from Elastic. However, even with how it's stored, even if it's not a recommendation, for this small environment we have here, it's stable enough. It's working.

    What do I think about the scalability of the solution?

    We're in the very early stages of usage. We only have maybe 20 people on the solution currently. We are increasing this, however. There will be more.

    The solution is easy to scale. You can add new Elasticsearch clusters. It should be noted that you have to separate the different roles from Elasticsearch to other devices, so you need a little bit more knowledge to do it right.

    How are customer service and technical support?

    We've been in touch with technical support a little bit as we're still in negotiation. Right now, we are running the basic product which is free of charge. We're in negotiation with the vendor for a license, where we will get proper support. We need it.

    Which solution did I use previously and why did I switch?

    I'm also familiar with Splunk, which is more expensive.

    How was the initial setup?

    In our case, it was a simple installation process. It was just set up in small environments, however, if it's getting larger, it will be more complex as then you have to split all the different roles onto different machines, to get the performance you need.

    Therefore, for small environments, it's very easy. If you're doing a big environment, then it's much more complex.

    The only maintenance needed is for updating the systems. We're working on it to make it all more or less automatic. All we need to do is to implement the updates when they arrive.

    What about the implementation team?

    We just handled the initial setup internally. We did not need the assistance of any integrators or consultants. 

    What's my experience with pricing, setup cost, and licensing?

    It's a bit too expensive, however, it's not as expensive as Splunk, which is a good thing. It's okay. There are cheaper products that we know, however, this is a very rich product, and it's got a very wide functionality, and a wide range of functionalities which I don't see in the other products, especially not in the cheaper ones.

    What other advice do I have?

    I'm just a customer and an end-user.

    Our company is always using the latest updates.

    I'd advise new users that you need to do a POC or get a test installation. It's free of charge. It's important to ingest a lot of data so that you get a feeling of scalability and performance. To put something in your lab, for example, is very helpful. It's only when you have data in the system, that you can see the benefits of the Elastic environment.

    I would absolutely recommend the solution to others. I'd rate it at a nine out of ten. I've been pleased with its capabilities overall. 

    Which deployment model are you using for this solution?

    On-premises
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Security Architect at a tech services company with 51-200 employees
    Real User
    Top 10Leaderboard
    Highly extensible, feature rich, and useful online documentation
    Pros and Cons
    • "The most valuable features of Elastic Enterprise Search are it's cloud-ready and we do a lot of infrastructure as code. By using ELK, we're able to deploy the solution as part of our ISC deployment."
    • "There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone."

    What is our primary use case?

    Elastic Search is added advantage for us because we normally use it for our uptime monitoring and our log analysis. When we merge it with Splunk, it helps us correlate and do security monitoring. 

    Elastic Enterprise Search comes embedded within a solution that we have developed for our clients. It's a payment solution. We've recently shipped it with Elastic Enterprise Search embedded. All the logs and all the internal communications get captured by Elastic Enterprise Search. It makes it easy for the IT teams who are doing uptime monitoring and troubleshooting to have a look at it. We have the security teams develop their own monitoring metrics and logs, if they wish, based on their deployment. 

    The beauty of Elastic Enterprise Search is if they also have their own third-party tools, there's the ability to integrate and read off Elastic Enterprise Search and have any third-party tool process the logs as well. It is highly extensible.

    What is most valuable?

    The most valuable features of Elastic Enterprise Search are it's cloud-ready and we do a lot of infrastructure as code. By using ELK, we're able to deploy the solution as part of our ISC deployment. 

    The extensibility and configurability of the solution are great. Having the ability to mine for anything is useful. It's extensible and useful in terms of digesting any type of information. Since we do a lot of consulting, it means we are able to apply it to diverse environments without having to suffer the overhead of integration.

    What needs improvement?

    There is another solution I'm testing which has a 500 record limit when you do a search on Elastic Enterprise Search. That's the only area in which I'm not sure whether it's a limitation on our end in terms of knowledge or a technical limitation from Elastic Enterprise Search. There is another solution we are looking at that rides on Elastic Enterprise Search. And the limit is for any sort of records that you're doing or data analysis you're trying to do, you can only extract 500 records at a time. I know the open-source nature has a lot of limitations, Otherwise, Elastic Enterprise Search is a fantastic solution and I'd recommend it to anyone.

    For how long have I used the solution?

    I have been using Elastic Enterprise Search for approximately four years.

    What do I think about the stability of the solution?

    I have no complaints in terms of stability. However, you have to make sure you give Elastic Enterprise Search the minimum resources it requires. We have not seen any major issues that we would send back to the vendor or the solution maker. If there was an issue it most likely would be from the environment, depending on how it was deployed and how it was configured.

    What do I think about the scalability of the solution?

    Elastic Enterprise Search is scalable. In our environment, we deploy it in a containerized environment. For us, we've experienced the scalability of the solution because as we grow and expand, we spin up more containers that are interconnected. I don't see any issues with Elastic Enterprise Search from a scalability perspective. 

    How are customer service and support?

    There's a lot of material available online. We tend to look online before we reach out for technical support. We have not needed to contact the support and this is a testament to how much information is available online. 

    What's my experience with pricing, setup cost, and licensing?

    The solution is not expensive because users have the option of choosing the managed or the subscription model. 

    What other advice do I have?

    Elastic Enterprise Search is a very good solution and they should keep doing good work.

    I'm a very satisfied customer because almost everything I need comes out of the book. You already have machine learning, alerts, the ability to search, APIs, inbuilt security, and integration to third-party authentication.

    I rate Elastic Enterprise Search a ten out of ten.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Flag as inappropriate
    PeerSpot user
    Senior DevOps Engineer at a financial services firm with 10,001+ employees
    Real User
    Offers certain log filtering capabilities and we can vet what we push into our database
    Pros and Cons
    • "The solution is quite scalable and this is one of its advantages."
    • "There is an index issue in which the data starts to crash as it increases."

    What is our primary use case?

    While the solution is slated for making logging positions more centralized, at present we are gearing through it. A fully-fledged deployment of alignments is not yet in place.

    We have adjusted the logs into the spec for a couple of our applications.

    What is most valuable?

    We consider all of the features to be valuable. With respect to 12B Kibana, all of the components fit in very well. Logsearch gives us certain log filtering capabilities and we can vet what we push into our database. This allows us only to log and ship limited items. Essentially, Logsearch plays a big role although not the most important one. 

    What needs improvement?

    The solution itself needs improvement. There is an index issue in which the data starts to crash as it increases.

    This leads to an impact on the solution's stability.

    The index and part of the solution's stage have weak points.

    In the next release, I would like to see better plugins when integrating with, say, Microsoft Teams.

    The Kibana dashboard is quite user-friendly and we have had no issues involving our technical team. However, some technical knowledge is required, especially if one wishes to create dashboards and as it relates to index management.

    For how long have I used the solution?

    I have been Vusing ELK Elasticsearch for plus or minus two years.

    What do I think about the stability of the solution?

    ELK Elasticsearch is definitely a stable solution. It is the spec that surprises most of the other logging solutions in the market.

    What do I think about the scalability of the solution?

    The solution is quite scalable and this is one of its advantages. We are trying to add or plug on to Elasticsearch at present.

    How are customer service and technical support?

    We have been open to solutions and haven't really had a need to rely on technical support. We've relied mostly on support forums.

    This said, I would rate the support well, as we initially interacted with the support team and made use of Google.

    How was the initial setup?

    The initial setup had a bit of a learning curve for us while we acclimated ourselves to the use of the solution. However, after a while, it became quite easy. 

    I would not say there was much complexity even at the outset, as we have an understanding of how to troubleshoot and do the installation.

    There is more than enough documentation of the solution online. It is useful and you can find what you're looking for. There are also forums that can be of assistance. 

    What other advice do I have?

    While I cannot say for sure, as our organization is structured so that we work in silos with everyone looking after his own infrastructure, I would estimate that we have approximately 200 employees making use of the solution.

    My advice to others who are considering implementing the solution is that they first make a plan to figure out how they wish to cluster the solution and the amount of data that must be ingested. Much planning would be involved. It would be wise to start with the open-source solution, which comes with many advantages, and to move on to the Enterprise version if there should be a need for dedicated support. 

    I cannot posit whether management will wish to take this route, although this is definitely worth considering, as we are talking about a fully robust infinite solution across the board. 

    I rate ELK Elasticsearch an eight out of ten.

    Which deployment model are you using for this solution?

    On-premises
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Oscar Narvaez - PeerSpot reviewer
    COE Head at a tech services company with 1,001-5,000 employees
    Real User
    Top 20
    Powerful with great integrations and good platform capacity
    Pros and Cons
    • "Search is really powerful."
    • "We'd like more user-friendly integrations."

    What is our primary use case?

    All my use cases have been based more on observability for IT operations. We deal with it in terms of metrics, logs, transactions, traces, and so on. 

    In terms of enterprise, most of the use cases are based on search capacity within the company to find documents and relevant information. That is the main use case.

    What is most valuable?

    The most relevant feature for me is the platform capacity. I consider the capacity high-performance with a distributed model that can support it, and recently we are growing. 

    Search is really powerful. All the search engines and the rules that complement them allow the users to create different kinds of administration for the platform. YOu can create synonyms or rules to better understand or to better detect partial search criteria. It's like an AI that boosts searchability.  

    The platform has a powerful tool to correlate and create rules that understand what people will be searching for. 

    All the community support that we have available from different users in the open source community is great. Everyone shares and publishes all of these different use cases. That makes the platform and the platform understanding really powerful for anyone who wants to implement a different case.

    It is easy to set up.

    The solution scales well. 

    They have great integrations on offer. 

    What needs improvement?

    Maybe Elastic Search could improve the analytics part of the search so it can be more powerful to the user. It could help provide more understanding of what people are searching for. 

    We'd like more user-friendly integrations. It should be easier for non-technical people to understand how to handle them. 

    For how long have I used the solution?

    I've used the solution for the last four years or so. 

    What do I think about the stability of the solution?

    It's stable. We have on-premise and on-cloud deployments. It's stable on both. I prefer the cloud as I avoid the time it takes to manage the platform. However, both cases are stable.

    What do I think about the scalability of the solution?

    It is a product that can scale well. It's not a problem. 

    We have maybe 200 people on the product right now. 

    How are customer service and support?

    I have experience working with technical support. They are good at responding to incidents. I have not had too many incidents, however, sometimes for probably technical questions in terms of platform performance, search, cluster distribution, and so on, I might reach out. 

    My point of view is that the technical support is awesome. They are very responsive and they have a really high understanding. The team has a lot of people with a lot of technical skills and technical knowledge.

    How was the initial setup?

    The initial setup is very straightforward. It's not difficult as well. 

    What's my experience with pricing, setup cost, and licensing?

    As I use the cloud, all of the costs for me are based on customer needs. There is a fascinating calculator published in Elastic. That there is not a specific starting cost. It can move from $10,000 US Dollars per year to any price based on how powerful you need the searches to be and the capacity in terms of storage and process. That said, you can start with a small budget, implement the use cases, and start growing slowly.

    What other advice do I have?

    I'd rate the solution nine out of ten. 

    I'm a customer and end-user. 

    Which deployment model are you using for this solution?

    Public Cloud
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    Enrique Peragallo - PeerSpot reviewer
    General Manager at Andes Tecnología y Consultoría Ltda.
    Real User
    Helpful in making calculations and monitoring variables, but there is a lack of technical people with experience
    Pros and Cons
    • "A nonstructured database that can manage large amounts of nonstructured data."
    • "There is a lack of technical people to develop, implement and optimize equipment operation and web queries."

    What is our primary use case?

    Elastic Enterprise Search is the repository for time series and data from the onsite instrument that monitors variables in our mining infrastructure called tailing dams. We monitor the tailing dams' physical stability and take the information from the sales force and manual data introduced by the operators. The system captures the information in the Elastic Enterprise Searchtime series, and we make calculations and trigger events and alerts based on those calculations. We save them as well as the events and alert times.

    What is most valuable?

    Elastic Enterprise Search is a nonstructured database that can manage large amounts of nonstructured data. We also use a structured SQL database. I am unsure why our technical people selected Elastic Enterprise Search. The people that started the project selected open-source software and recommended the ETC component required in the system architecture. The Elastic Enterprise Search has been defined from the beginning of the project and fulfills the project's requirements. However, there is a lack of technical people to develop, implement and optimize equipment operation and web queries. This may be a problem with the provider, and they currently lack the resource to optimize the performance of the database.

    What needs improvement?

    Finding skilled people to work with Elastic Enterprise Search in the project team has been difficult. This may be because the development team has not considered it. It is important to improve the database performance because there is a large amount of data and the optimization of the queries and the system's performance are very important.

    We also use three other databases, MinIO, PostgreSQL and PostgreSQL. We have a very skilled person on our team that knows how to use all these products. However, he's not responsible for optimization because it's the responsibility of the Indian provider that has to develop the application.

    What do I think about the stability of the solution?

    It is fairly stable.

    What do I think about the scalability of the solution?

    It is a scalable solution. 70 people are working with this solution in the project, 35 on the development team and 20 backend people. We are working on the development, but it's part of the service that the Indian company has to provide. There are about 50 people on their development team who deal with all the development, infrastructure implementation, architecture definition and implementation of the software stack. We are the counterpart of that company.

    What's my experience with pricing, setup cost, and licensing?

    Since it is open-source, we don't pay licensing fees. In the development and QA environment, we don't pay anything. We, however, have to pay for all the software, subscription, pre-protection and protection.

    What other advice do I have?

    I rate this solution a seven out of ten. Because it is open-source, there is no technical support provided by the vendor, so we are moving to enterprise subscriptions for each of these products. We are allowed free licenses and implement enterprise or commercial licenses and the production of protections.

    An original criterion selects the software stack because they have to be good tools, but they all have to be open-source. Nobody considers it because the original team that started the project worked in an investigation organization and was closer to open-source software.

    They are not clear regarding the support of their solution when they go into production. That's why we are updating the licenses to interpret license subscriptions and assume their support for each software component.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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