Of course, the ELK stack is open source. Since IT organizations prefer open source products, this alone could explain the popularity of the stack. By using open source, companies can avoid retaining suppliers and onboard new talent much more easily. Everyone knows how to use Kibana, right? Open source also means that a vibrant community is constantly driving new features and innovations and helping when needed. There are several common and sometimes critical mistakes that users make when using the different components of the stack. Some are extremely simple and include basic configurations, others relate to best practices. In this section of the guide, we will describe some of these mistakes and how you can avoid them. Before you decide to configure the stack, you should first understand your specific use case. This directly affects almost every step implemented along the way – where and how to install the stack, how to configure your Elasticsearch cluster and what resources to allocate to it, how to create data pipelines, how to back up the installation – the list is endless.
There are several ways to use this safety net, both in Logstash and in some cases where middleware components are added to your stack. Below is a list of some best practices you can use to avoid some of the most common logstash pitfalls: In general, a production-grade ELK implementation must meet some basic requirements: The ELK stack can go a long way in achieving SIEM. Let`s take the example of an AWS-based environment. Organizations that use AWS services have a large number of auditing and logging tools that generate log data, audit information, and details about changes to the service configuration. These distributed data sources can be leveraged and used together to get a correct, centralized security view of the stack. Until a year or two ago, the ELK stack was a collection of three open source products – Elasticsearch, Logstash, and Kibana – all developed, managed, and maintained by Elastic. The introduction and subsequent addition of beats turned the stack into a four-legged project. Based on my previous article, Introduction to ELK, I thought it would be great to discuss how to create a “stack”.
I`ve created several different stacks over the past few months, each with its own specific purpose. While the services within an ELK stack are meant to be spread across different nodes, creating a “single node” stack can be a great easy way to get directly familiar with the capabilities of Elasticsearch, Logstash, and Kibana. The ELK stack is most often used as a log analysis tool. Its popularity lies in the fact that it provides a reliable and relatively scalable way to aggregate, store and analyze data from multiple sources. Therefore, the stack is used for a variety of different use cases and purposes, ranging from development and monitoring to security and compliance, SEO, and BI. Despite the fact that ELK as a standalone stack doesn`t have built-in security features, the fact that it allows you to centralize logging from your environment and create dashboards focused on monitoring and security has led to the stack being integrated with some leading security standards. This is one of the main problems not only for working with Logstash, but for the entire stack. If all of your ELK-based pipelines are shut down due to a faulty Logstash configuration error, this is not uncommon. No matter where you deploy your ELK stack, whether on AWS, GCP, or in your own datacenter, we recommend that you create a cluster of Elasticsearch nodes running in different Availability Zones or in different segments of a datacenter to ensure high availability.
ECE has specific hardware requirements for memory and storage. The hosts that you use must support the x86-64 instruction set. I need to create an ELK architecture, but I don`t know how many servers and requests (CPU, RAM, disk space) I will need. I need to send syslog and log files from 15 servers (about 500 MB/day in total) to this ELK and I have almost 60 days of retention.