Senior Application Engineer at a comms service provider with 11-50 employees
Real User
Top 5
2025-06-20T11:54:48Z
Jun 20, 2025
I use Logstash in real-time. Logstash in my current architecture is not used as a stand-alone product; it works with Filebeat and Elastic, and we use Kibana as a visualization tool. We have logs that are real-time in integration servers, and these logs are shipped to Elastic with our implementation of transformations. The transformation means we ship the logs in the way that we want them to be presented in Kibana, which is the main function we use Logstash for. I'm not working with any queuing mechanism in Logstash. The deployment mechanism of Logstash involves deploying it once; we deployed it on-premises and not in the cloud. If we have any changes in logs, we make changes in the configuration file, and then we redeploy this configuration file. The deployment of the product itself is an RPM package that is deployed in a server, and after that, my role is to create a configuration file based on my log architecture. The deployment for Logstash has no complex aspects. There are no improvements needed for Logstash; it's already working well for us. On a scale of 1-10, I rate Logstash a 10 out of 10.
Business Unit Head at Cyber Knight Technologies FZ LLC
Reseller
Top 20
2025-03-11T12:25:54Z
Mar 11, 2025
I would recommend Logstash to DevOps engineers but not to other audiences like decision-makers, business people, IT managers, or developers due to its technical complexity. I rate the solution a seven out of ten.
Almost seven or eight features were good. Importing functionality must be upgraded. We need to create this long ingest pipeline set. That's a problem at the moment. Overall, I rate the solution seven out of ten.
Log Management is the practice of collecting, storing, and analyzing log data from various sources within an IT environment to improve security, compliance, and operational efficiency.
Efficient Log Management allows organizations to detect anomalies, troubleshoot issues, and ensure compliance with industry regulations. Logs come from diverse sources, including servers, applications, and network devices. Handling and analyzing this data effectively can offer significant insights into system...
I use Logstash in real-time. Logstash in my current architecture is not used as a stand-alone product; it works with Filebeat and Elastic, and we use Kibana as a visualization tool. We have logs that are real-time in integration servers, and these logs are shipped to Elastic with our implementation of transformations. The transformation means we ship the logs in the way that we want them to be presented in Kibana, which is the main function we use Logstash for. I'm not working with any queuing mechanism in Logstash. The deployment mechanism of Logstash involves deploying it once; we deployed it on-premises and not in the cloud. If we have any changes in logs, we make changes in the configuration file, and then we redeploy this configuration file. The deployment of the product itself is an RPM package that is deployed in a server, and after that, my role is to create a configuration file based on my log architecture. The deployment for Logstash has no complex aspects. There are no improvements needed for Logstash; it's already working well for us. On a scale of 1-10, I rate Logstash a 10 out of 10.
I would recommend Logstash to DevOps engineers but not to other audiences like decision-makers, business people, IT managers, or developers due to its technical complexity. I rate the solution a seven out of ten.
Almost seven or eight features were good. Importing functionality must be upgraded. We need to create this long ingest pipeline set. That's a problem at the moment. Overall, I rate the solution seven out of ten.
I rate the overall product a ten out of ten.