In this chapter, we introduced you to Elastic Stack, where we discussed the different components of Elastic Stack, such as Elasticsearch, Logstash, Kibana, and different Beats. Then we looked at different use cases of Elastic Stack, such as System Performance Monitoring, where we monitor the system's performance, Log Management, where we collect different logs and monitor them from a central place, and Application Performance Monitoring, where we monitor our application by connecting it to a central APM server. We also covered Application Data Analysis, where we analyze the application's data, Security Monitoring and Alerting, where we secure our stack using X-Pack, monitor it regularly, and configure alerts to keep an eye on changes that can impact the system's performance, and Data Visualization, where we use Kibana to create different types of visualizations using the available data.
In the next chapter, we'll cover different methods of pushing data into Kibana, such as from RDBMS, files, system metrics, CSV, and applications. We'll start with different Beats to demonstrate the complete process of configuring these Beats and sending data directly to Elasticsearch or via Logstash to Elasticsearch. Then, we'll look at how to import data from CSV by configuring Logstash to take input and insert data into Elasticsearch. After CSV, we'll fetch data from RDBMS using SQL queries through the JDBC plugin and insert it into Elasticsearch. We'll use the preceding methods to insert data into Elasticsearch, and then we'll configure Kibana to fetch the data by creating an index pattern. In this way, we can fetch any type of data into Kibana and can then perform different operations on that data.
