HARDCODE

    Logstash did a very impressive job transforming our logs into documents that allowed us to understand and visualize how our applications are behaving. But, because of that Logstash could require quite the memory and CPU to run. So, it's not the most efficient move to make Logstash collect the data from various sources. Elastic offered a solution to this concern and introduced Beats . Beats  is a lightweight shipper for forwarding and centralizing log data. It's installed as an agent on your servers to capture all sorts of operational data like logs or network packet data. Beats is great for gathering data and works efficiently with a large number of files. It can also handle back pressure (when Logstash is busy) and ensures that no data is lost during such periods. And to be clear Logstash can do most of what Beats . So, why use Beats instead? 1. Lightweight Data Shipping : Beats is designed to be lightweight and requires fewer resources than Logstash . This makes it ...

 What's the first thing you imagine when you hear the word logs . I bet you pictured a huge amount of text that's hard to read and it just gives you a headache every time you try to tell the lines apart. And if you're looking for an error or something, it feels like you're looking for a needle in a haystack. So, how can you transform these logs from clunky text which is hard to read into documents in your Elasticsearch index? The answer lies in the letter L in the ELK stack. We've talked about the E , we've talked about the K , now it's time to talk about L : Logstash . Logstash is a powerful open-source data processing pipeline tool that collects data, transforms it into a common format, and sends it to a destination for storage or further analysis. And as I previously pointed out in the previous posts: the great advantage of using this stack together is the seamless integration that allows you to save the effort of integration. You can look at Logstash a...

   In my previous post  Introduction to Elasticsearch: Create, Update, Delete and Search Documents 🔍  I showed you how to set up an Elasticsearch index and manage its data. But as you could see in that post the data looked very ugly on the cmd window. That's because this is not Elasticsearch 's job. Data representation and visualization is key to understanding your data and even predicting its trend. And as I previously said, the power in Elasticsearch lies in its integration with other powerful tools such as our guest of honor: Kibana . Kibana , developed by Elastic , is a powerful open-source data visualization and exploration platform. It seamlessly integrates with Elasticsearch, making it an essential component of the Elastic Stack. Whether you’re a data analyst, developer, or business user, Kibana empowers you to unlock valuable insights from your data. With its intuitive interface, you can create interactive dashboards, explore logs, analyze metrics, and visu...