Elastic Stack reviews

One of the best features I like is that Elastic built their own kubernetes operator to extend the k8s orchestration and make it easy to deploy, scale, change, secure and configure hot-warm infrastructures. Their operator saves a ton of time during configuration. I have deployed stacks on different k8s architectures like Azure Kubernetes Service, Amazon Elastic Kubernetes Service and small on prem clusters with microk8s without issues. When we reach performance thresholds we add more elastic nodes and ECK secures and joins it to the cluster and in minutes we can leverage the extra compute. A lot of changes that are done after going to PROD are non-disruptive since ECK is aware of the main node and makes sure to pass the master role before the main one is re-deployed. I have also migrated Elastic Cloud Enterprise deployments running on bare metal and the stability of ECK is unmatched.

Currently it is not recommended or supported for a PROD cluster to do its own self monitoring so you have to deploy a monitoring cluster. In cloud scenarios this adds costs and extra complexity so it will be great to have this feature supported.

I mostly like the Elastic Search queries. Those follow a certain standard and work very well. Data retrieval is very easy and fast in Elastic Search.Visualizing data thru Kibana is very smooth and straightforward. It supports a couple of inbuilt dashboards to represent the data nicely.

Nothing to dislike about Elastic Stack. The only thing is configuration and setup should be correct. It could take some time for beginners.

It is a super fast and efficient data extraction tool. Recommended for medium-sized projects.
Handles large amounts of data, is scalable.

Usable from any device, however these must be state-of-the-art and offer great calculation speeds and ram storage.

It can handle large volumes of data and scale to meet the needs of growing businesses. The stack allows for near real-time data analysis, providing businesses with the ability to make quick decisions based on current data. Elastic stack is open source, which means that it can be customized and extended to meet specific business needs. Elastic Stack has a large and active community of users and developers, providing a wealth of resources and support for businesses.

The Elastic Stack is a powerful tool, but it can be complex to set up and manage, especially for users with limited technical expertise

1.Allows Faster searching and query operations
2.Provides with easy data visualization for analysis
3.Support for multiple data sources
4.Good SDK support for quick integration with application
5.Scalable as per the requirement with support of kubernetes

1.UI is simple ,could be made more robust and dynamic
2.Calculations and processing speed can be further improved
3.Proper usage knowledge is required when using it on scalable platforms

Searching is where elasticsearch is second to none, either in terms, n-grams or full-text. Latest releases have greatly improved the aggregation performance, so it's also a great fit for analytics workloads. The customizable sharding and replica configurations make is very reliable too.

Searching and joining different documents has room for improvement, it's usualy not as fast as we would like it to be, so most of the times we end up un-normalizing documents and en-richening their data to boost searching performance.

It is very easy to deploy using docker containers and Kubernetes support. It's very easy to query a high volume of data and get results in seconds. Its easy-to-use UI is self-explanatory and also the forum support and documentation are quite helpful.

Sometimes it crashes in between. Query structure is somewhat completed, and can be made simple with more specific examples to use directly in projects.

Elasticsearch simplifies data queries and integration with user interfaces. Describing the records as JSON makes updates as simple as modifying your JSON format. The query builders make selecting complex data relationships easy to construct.

As designed and by default, Elastcsearch does not immediately synchronize data between server nodes. Updates and inserts to records take time to reconcile which can cause some data integrity issues with frequent updates. ES is not the best tool to take bulk or rapid changes to the data. It is a great tool for retrieving data. The best combination is to use a SQL database for CRUD operations and replicate/synchronize the data to Elasticsearch for user interface/reporting.

Natural language processing like stemming, removing stopwords, multilingual search, tokenized storage etc.

When there is a large text searches, the elastic search sometime crashes. This needs to be optimized.

Elastic search is very fast, it returns the results within a few milliseconds. We can connect Logstash and Kibana very easily with Elastic.The filters in Kibana are very good. We can sort the data as needed.

I don't think there is anything to dislike about Elastic.

Elasticsearch is quite powerful and fast. You can implement it to any enterprise software independent of the scale. It's well documented and getting frequent updates. It's also a reliable software that you can use in mission-critical operations.

Breaking changes between different versions are hard to deal with. Each major version upgrade of ElasticSearch is bringing new functionality, improved security, and speed - but at the same time, it requires you to update your indexes, which is not an easy task if you don't have a strong infrastructure team.

Flexibility and high performance are the most loved features for us. The fact that we are not using it very effectively is also a ramen of suggestions and guidance.

The only feature I don't like is that it is Java based.

Elasticsearch offers a very flexible system for adding search capability to your systems. It is also capable of much more. The REST API and great documentation makes getting started very simple. Elasticsearch was also designed with scaling in mind. Adding nodes and self balancing is quite easy. AWS offers hosted Elasticsearch that makes spinning up your first cluster as simple as a few clicks.

Writing complicated queries can be quite tedious at times. The JSON interface is not always easy to read when trying to match up parentheses. Upgrading from older versions is not a simple process.

1. End to End Solution of enterprise logs with services such as Logstash, Elastic and Kibana.
2. Strong User Community and support.
3. Easy to use and implement.
4. Proactive updates on possible downtimes.
5. Dashboards for easy navigation.

1. Cloud performance is slower than on premises installation.
2. It crashes in between which delay things sometimes.

Provides great features for log ingestion, normalisation and visualisation.
Has a free open source tier which can be used to cover many use cases.
Visualisation options are diverse and powerful.
Solid community support in forums.

Initial setup can be tedious and is rather complex.
The provided security ruleset tends to produce many false positives and requires fine tuning.
Log ingestion options are not entirely covered by the web user interface.

Die search engine ist der Hammer. Die Software funktioniert gut, zuverlässig und performant.

In 4 Jahren hat 3 mal der Firmensitz gewechselt, letztes mal ins Ausland. Cowboy-Manieren beim Vertragsverlängerung - friss unsere AGBs (in Business-English) oder stirb. Ständig wechselnde Ansprechpartner. Rechnungen mit wechselnden Kontoverbindungen.

We are using ELK Stack SIEM, a scalable aggregation engine that helps us find patterns in our data as well as provides a wide array of customizable analytics and reports. With Elastic Stack, we have a better understanding of flaws and security issues, particularly memory tier support that ensures the security of our data and networks.

It is very challenging and time-consuming to upgrade cluster node roles and data replication.

Elasticsearch is the single most valuable tool I have come across in my career for solving big data problems. No other datastore scales as well and as easily as ES. The premium features that come with a license are extremely powerful and definitely make a case for upgrading beyond just the need for support like most database solutions.

Elasticsearch definitely has a significant learning curve for developers and administrators experienced with a more relational database solution. However with some time and with the aid of the fantastic UI Kibana these hurdles are small in comparison to the power you can reap.

the very powerful search engine I search around 5 million log in a second only.
Very nice real-time dashboard, I can integrated with other software and see all the view of network traffic, traffic flow very well.

well I would say, it's pretty good already, but still some function need to use command alot.

This is best search software for data searching and analytics on stream data in cloud. It is so flexible complex keyword searches and very efficient. Great tool for analyzing logs very powerful. 24/7 customer support.

Not really a negative side but if you have low bandwidth web interface will become very slow.And think learning manual still needs some improvement.

Lightening fast, we built BI dashboards and the 5 years reports data gets populated within seconds that would otherwise takes hours traditionally database or BI tool

Very Big learning curve, first tie deployment takes very long time to make it work perfectly and then optimize it. need very good technical expertise

We user Kibana's dashboard and also part of it for data analysis of our tool. It allows us to look and compare historical data and gives us a great visualisation, analytic tools and data sharing options of all the data collected by our scanner.

I enjoy working with the tool, however, I am missing quite some UX design improvements that could really improve the interaction with Kibana. Moreover, I could use an option of saving the templates or most recent types of searches into some kind of database so we would not need to setup these all over again.

When you want to run queries in big data and get results, you can get results much faster from relational databases. Queries are also easy to create

We sometimes have problems during the integration of Kibana. But we couldn't figure out why
Other than that, I didn't have any problems

- Almost 100% of uptime
- Great support
- Really fast and easy to use for any application
- Easy to configure

- The cost of product may inviabilize it's use for small applications or companies
- If the configuration goes wrong it may really affect the speed

The search is very good and very fast in response.
Documentation is very good for writing NoSQL queries.
Libraries are there for 90% of popular languages.

It would be good to create a standardization for NoSQL.
It would be great, Elastic search provides IDE to write the queries rather than editors.