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.

Handles a ton of traffic without issues given enough hardware

Hardware requirements might be too much for little deployments.

Using elastic stack I can easily track my application logs.

I liked almost all the features but there is still scope in the dashboard.

I really like how multiple inputs can be managed at the same time.

The multiple versions of API and multiple possible format make it quite hard to get the correct one - mostly when a new release is out and user is pushed to use it - without proper support.

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.

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

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.

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.

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.

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.

It is good to store and monitor logs with Kibana.

The elasticsearch version management is bad because next version could have critical changes from previous.

- 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