In any large organization, there will be different servers with different sets of applications. So, in this case, we need to have different teams for different applications whose task is to explore the log files for debugging any issue. However, this is not an easy task, as the format of logs is never user friendly. Here, I am talking about a single application, but what will happen if we ask the team to monitor all different applications that are built using different technologies and their log format is very different from other applications? The answer is very simple: the team has to dig through all the logs from the different servers and then they will spend days and nights to find the issue. ELK Stack is very useful for these situations, and we can solve this problem easily. First of all, we need to set up a central Elasticsearch cluster for collecting all different logs. Now, we need to configure Logstash as per the application log so that we can transform different log formats that we are getting from different application servers. Logstash will output this data into Elasticsearch for storage so that we can explore, search, and update the data. Finally, Kibana can be used to display graphical dashboards on top of Elasticsearch. Using this setup, anyone can get complete control of all logs coming from different sources. We can use Kibana to alert us to any issues in the log file so that the user can get the issue without doing any data drill downs. Many organizations are using ELK for their log management as this is an open source software that can be built easily to monitor different type of logs on a single screen. Not only can we monitor all of our logs in a single screen, but we can also get alerts if something went wrong in the logs.

Security monitoring and alerting is a very important use case of ELK Stack as application security is a vital part, and it costs if there are any security breaches in the application since security breaches are becoming more common, and most importantly, more targeted. Although enterprises are regularly trying to improve their security measures, hackers are successful in penetrating the security layers. Therefore, it is very much required for any enterprise to detect the presence of security attacks on their server, and not only detect but also alert them so that they can take immediate actions to mitigate their losses. Using ELK Stack, we can monitor various things, such as unusual server requests and any suspicious traffic. We can gather security-related log information that can be monitored by security teams to check any alerts to the system.

This way, security teams can prevent the enterprise from attackers who have gone unnoticed for a long time. ELK Stack provides a way through which we can gain an insight and make the attacker's life more difficult. These logs can also be very useful for after-attack analysis; for example, for finding out the time of the attack and the method of attack used. We can understand the activities the attacker performed to attack, and this information can provide us with a way to strengthen that loophole easily. In this way, ELK Stack is useful for both before attack prevention and after attack healing and prevention.

In ELK Stack, we have different tools to grab data from remote servers. In traditional Relational Database Management System (RDBMS), it is quite difficult to save these types of data because they are not structured, so either we have to manually clean the data or leave some part of it in order to save it in the table schema. In the case of Elasticsearch, the schemaless behavior gives us the leverage to push any data from any source. It not only holds that data but also provides us with a feature to search and play with it. An example of web scraping using ELK Stack is a Twitter to Elasticsearch connector, which allows us to set up hashtags from Twitter and grab all the tweets that used those hashtags. After grabbing those hashtags, we can search, visualize, and analyze them in Kibana.

Many of the top e-commerce websites, such as eBay's, are using Elasticsearch for their product search pages. The main reason behind this is the ability of Elasticsearch in full-text searching, building filters, facets, aggregations, fast response time, and the ease it provides in collecting analytic information. Users can easily drill down to get the product set, from where they can easily select the product they want. This is just one side of the picture, through which we are improving the user's experience. On the other side, we can use the same data and by using Kibana, we can monitor the trends, analyze the data, and much more. There is a big competition going on among e-commerce companies to attract more and more customers. Being able to understand the shopping behavior of their customers is a very important feature, as it leverages e-commerce companies to target users with products that they had liked or will like. This is business intelligence, and using ELK Stack, they can achieve it.

ELK Stack's core competency is its full text search feature. It is powerful and flexible, and it provides various features such as fuzzy search, conditional searching, and natural language searching. So, as per our requirements, we can decide which type of searching is required. We can use ELK Stack's full text search capabilities for product searching, autocomplete features, searching text in emails, and so on.

Kibana is an easy-to-use visualization tool that provides us with a rich feature set to create beautiful charts (such as pie charts, bar charts, and stack charts), histograms, geo maps, word tags, data tables, and so on. Visualizing data is always beneficial for any organization as it helps top management to make decisions with ease. We can also easily track any unusual trends and find any outliers in data without digging into the data. We can create dashboards for any existing web-based application as well by simply pushing the application data into Elasticsearch and then use Kibana to create beautiful dashboards. This way, we can plug in an additional dimension into the application and start monitoring it without putting any additional load on the application.