As you may very well know, Kibana currently has almost 20 different visualization types to choose from. This gives you a wide array of options to slice and dice your logs and metrics, and yet there are some cases where you might want to go beyond what is provided in these different visualizations and develop your own kind of visualization.
Kibana is the visualization layer of the ELK Stack — the world’s most popular log analysis platform which is comprised of Elasticsearch, Logstash, and Kibana. This tutorial will guide you through some of the basic steps for getting started with Kibana — installing Kibana, defining your first index pattern, and running searches. Examples are provided throughout, as well as tips and best practices.
Distributed tracing has become a de-facto standard for monitoring distributed architectures, helping engineers to pinpoint errors and identify performance bottlenecks. Zipkin is one of the popular open source “tracers” available in the market, and I’m now happy to inform our users that we’ve recently introduced a new integration that allows users to easily ship trace data collected by Zipkin to Logz.io!
Logstash is the “L” in the ELK Stack — the world’s most popular log analysis platform and is responsible for aggregating data from different sources, processing it, and sending it down the pipeline, usually to be directly indexed in Elasticsearch. Logstash can pull from almost any data source using input plugins, apply a wide variety of data transformations and enhancements using filter plugins, and ship the data to a large number of destinations using output plugins.
Logs are unpredictable. Following a production incident, and precisely when you need them the most, logs can suddenly surge and overwhelm your logging infrastructure. To protect Logstash and Elasticsearch against such data bursts, users deploy buffering mechanisms to act as message brokers. Apache Kafka is the most common broker solution deployed together the ELK Stack.
Today I have the immense privilege of sharing the exciting news that we have raised $52M in series D funding led by General Catalyst. I am thrilled that all of our existing investors share our vision and chose to invest further in the company.
Elasticsearch is the living heart of what is today’s the most popular log analytics platform — the ELK Stack (Elasticsearch, Logstash and Kibana). The role played by Elasticsearch is so central that it has become synonymous with the name of the stack itself. Used primarily for search and log analysis, Elasticsearch is today one of the most popular database systems available today.
Cloud security is becoming one of the most pressing issues for many modern organizations as they move to the cloud. According to Cloudneeti’s 2019 Cloud Security Trends and Predictions report, by 2020, 41% of overall workloads will run in public clouds. Defending against unauthorized data exposure and securing data, applications, and infrastructures across the cloud environment is a must. It is the responsibility of every organization and should be taken seriously.
At Logz.io we obsess over our customers and believe that our customers’ happiness is fundamental to our success as a company. With a growing number of customers all over the world, it’s important to us that we provide them with the best experience we possibly can. After all, we know we’re handling extremely important data and helping to keep mission-critical applications up and running.
Load balancers play a key component in any cloud-based deployment. By distributing incoming traffic across backend servers or services, load balancers help improve responsiveness and increase the availability of your applications. Monitoring load balancers is important for analyzing traffic patterns and troubleshooting performance and availability issues.
