Über Elastic Stack
SaaS-basierte Lösung zur Unterstützung der Datenvisualisierung durch Live-Präsentationen, KPIs, Heatmaps, Waffeldiagramme und mehr.
The flexibility, it's free, large community and plenty of tools.
We sometimes have problems during the integration of Kibana. But we couldn't figure out why.
Nutzerbewertungen filtern (56)
Tolles Produkt - Misserables Firmengebaren
Kommentare: Der Elastic Stack mit 15 Nodes betreiben wir OnPrem als zentrales Logfile-Monitoring für unzählige Systeme.
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.
Elastic Cloud on Kubernetes for best scalability
Kommentare: Organizing chat data to be searchable and log management to proactively fix issues.
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.
Elastic Stack for Application Logs
Kommentare: Elastic Stack is an open source full stack solution for logs of modern day big data applications processing logs with its different applications of Logstash, Elastic and Kibana. We are using it to read through application logs, storing logs data and using dashboard to easily navigate thru the big chunk of files. Its an amazing combo of applications, completely free of cost with easy implementation and powerful online support.
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.
This powerful tool allows you to take data from any source and format to search and analyze.
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.
Best for Website Monitoring, Event Management and Log Analysis.
Kommentare: This lightweight, yet powerful, modern SIEM is well-suited for the modern security operations center as it can handle a wide range of activities with ease. elastic Stack is a powerful and flexible SIEM with feature-rich out-of-the-box tools that make it easy to get the job.
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.
One of the Best No sql Document Base Database
Kommentare: In our projects, we use elasticsearch in database queries that need fast answers. It is especially useful for search and autocomplete operations.
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
Kommentare: I use it log search.
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.
Perfect combo for capturing, searching an managening your logs!
It has all the funtionalities I need for incident management. You can browse logs and create dashboards in order to help visualize the data
Honesty, I cannot point anything that I don't like. It's perfect for my day to day job
Very good log server
I use it for syslog and network monitoring. Data analysis and graphical representations are great.
Elk installation steps can be made easier.
Best Solution To Monitor Application Logs
Kommentare: Great features and functions have helped me a lot.
Managing application logs have become very easy with the help of Logstash, Elastic, and Kibana (ELK) since we can use the dashboard to visualize the performance and spot errors and performance issues. This has made my business move more faster and agile.
The cost may not be suitable for very small-scale companies but otherwise, I would say all the features are amazing.
A complete stack for full text search
Kommentare: Elasticsearch might be overkill if you are working with a small or mid-sized applications. However it's a serious solution for big-scale apps that are dealing with millions of records. The setup of the Elasticsearch might seem easy, but the maintenance is not. If you have simpler needs, I would suggest the full-text search functionality of modern PostgreSQL versions.
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.
Elasticsearch is a general purpose search engine that can do much more than search
Kommentare: We use Elasticsearch to filter and sort search results in our marketplace. We've built out many complicated queries that allow us to do interesting things like geo-based queries, personalization, and time boxed deals.
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.
Working On Big Data Is Now More Comfortable
Kommentare: Our company uses elasticsearch to analyze data in very large data. Successful indexing is designed in a cluster (node) structure, which has made our work much easier. Thanks to this search engine, we can reach the desired analysis results in the data. It is a blessing for our sector employees to have a free application running in this performance.
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.
The perfect searching allied to a RDB
Kommentare: We've been pairing Elasticsearch with a traditional RDB in many projects with great results. This way we don't compromise our data reliability and searching speed is blazing fast.
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.
Powerful stack for event collection, management and visualisation
Kommentare: A solid product with a rich feature set, if you get past the initial setup complexity.
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.
Elasticsearch Makes Big Data Possible
Kommentare: We've dramatically improved the stability of our big data analytics compared to any other data store we've used.
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.
Elastic Stack is the best for Business
Kommentare: we use elastic stack to integrated with elastiflow to track all the traffic destination, protocol log, and other activity inside network, it very nice and work well, and produce the nice dashboard and clear view.
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.
Distributed Search and analytics engine available in cloud
Kommentare: Best search and analytics software available in cloud, flexible to use and available free trial to evaluate it.
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.
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.
Have too much data to your database?
Kommentare: Our application makes many filters for too much data, without Elasticsearch it just stops
- 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
ElasticSearch: A Powerful Search Engine
Kommentare: Its a very good search engine as it has a power to query for more than thousands-lakhs of records in just few seconds and sometimes is perform better. But again its all depends on the query you have written. Sometimes writing a specific busniess query got harder over SQL queries.
Flexibility and high performance. Good for Distributed full text search, No-SQL, Aggregation. Can be used as a replacement MongoDB and RavenDB. Capability of handling mutiple type of data- including textual, numerical, geospatial, structured, and unstructured.
Mostly for developers, other people's would have a hard time to understand and work on it. Also the query is typical or hard to write in terms of SQL queries.
Best product for search and aggregations
Great full text capabilities.
Highly scale able .
Good set of libraries.
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.
Managing big databases
The product is very popular among many companies, therefore there is a big community who can share their knowledge, the search is very fast and the installation process and integration are very easy.
I leas like the Elasticsearch itself does not provide much except just storing the information, the additional tools (Kibana and Logstash) are required.
Excellent Tool When Used Correctly
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.
Its a really good solution for people looking to process large volumes of data, it allows to filter, make aggregations and other operations really fast even when you need to rely on text search.
Its really easy to make your performance really low, you have to be really careful with your cluster setup, mapping and queries.