The Elastic Stack can have multiple use cases, and we can use it in many areas, such as logging, data searching, and dashboarding; but these are just a few use cases of the Elastic Stack that we primarily use. There are many other areas where we can use the power of Elastic Stack. We can use the Elastic Stack for the following use cases.

We need to make our application stable by avoiding anything that can impact its performance. Anything that can hamper application performance, such as the system, database, or any third-party dependency. If anything fails, it will impact the application's performance. System monitoring using Elastic Stack can help us to avoid such situations where the system can impact application performance. There may be a number of reasons, such as if system memory or CPU is creating a bottleneck because of an increase in user hits. Using monitoring, we can configure the alert whenever the threshold value of any component increases. In this way, you can protect yourself from any application outage because of system performance.

Log management is one of the key use cases of Elastic Stack, and we have been using Elastic Stack for this purpose for many years. There are many benefits of log management using Elastic Stack. Let's say you have a log file and you need to explore it to get to the root cause of any issue in the application. So how are you going to proceed? One way is to open the log file in a text editor or terminal and search the issue. Another way is to push the log data into Elasticsearch and configure Kibana to read this data. We can use Filebeat to read the log files, such as Apache access and error logs. Apart from system logs, we can also configure Filebeat to capture application logs. This way, using Filebeat or Logstash, we can push the logs into Elasticsearch and can analyze that using Kibana.

Using Elastic Stack APM, we can monitor applications for performance and availability. APM helps us to identify any current application issues or ones that may occur in the near future. We can find and fix any bug in the code using APM, as this makes the problems in the code searchable. By configuring APM with our application, we can monitor the code and make it better and more efficient. Elastic APM also provides us with custom preconfigured dashboards in Kibana that we can easily load. We can apply machine learning to APM data using the APM UI to find any anomaly in the data. We can also configure the alerts so that we can get the email notification if anything goes wrong in the code. Currently, Elastic APM supports Node.js, Python, Java, Ruby, Go, and JavaScript. It's easy to configure APM with the application, and it requires only a few lines of code to configure.

Data visualization is the main feature of Kibana, and using Kibana, we can create different types of charts, graphs, and so on. Kibana is popular because it has the capability to create dashboards for KPIs using data from different sources. We can push any structured and unstructured data into Elasticsearch, using Logstash or Beats. Once the data is in Elasticsearch, we can create visualizations by creating index patterns in Kibana for those indexes in Elasticsearch.