CIOs see data costs as their greatest logging challenge to overcome, according to this survey we collaborated on with IDC. If you’re running significant production operations, you’re almost certainly generating 100’s of GB of log data every day. Naturally, you’re also monitoring those logs and querying for incident investigations. However, most log data is never queried or analyzed, yet makes up the majority of logging costs.
Running a successful company relies on current and accurate information about the underlying systems. Much of this information is contained within your application logs. By investing in your log management solution, you can unlock these crucial insights and access a wealth of powerful data. This post presents a series of goals that will allow you to make the best possible use of your application logs.
The ELK stack is an industry-recognized solution for centralizing logging, analyzing logs, and monitoring your system use and output. However, the challenges of maintaining your own stack overcoming common Elasticsearch problems need to be considered. The popularity of the ELK Stack can be boiled down to two, key tenets. First, the management and analysis of logs is an incessant issue for developers, SMBs, and enterprises alike.
Continuous Integration and Continuous Delivery (CI/CD) delivers services fast, effectively, and accurately. In doing so, CI/CD pipelines have become the mainstay of effective DevOps. But this process needs accurate, timely, contextual data if it’s to operate effectively. This critical data comes in the form of logs and this article will guide you through optimizing logs for CI/CD.
Microservice architecture is widely popular. The ease of building and maintaining apps, scaling CI/CD pipelines, as well as the flexibility it offers when it comes to pivoting technologies are some of the main reasons companies like Uber and Netflix are all in on this approach. As the amount of services in a microservice architecture rises, complexity naturally also rises.
Elasticsearch is a complex piece of software by itself, but complexity is further increased when you spin up multiple instances to form a cluster. This complexity comes with the risk of things going wrong. In this lesson, we’re going to explore some common Elasticsearch problems that you’re likely to encounter on your Elasticsearch journey.
The modern enterprise has expanded its reach by using the power of cloud computing. However, with that power comes complexity in leveraging the multiple platforms needed to provide rich functionality. To achieve a seamless integration that involves multiple cloud infrastructures you need insightful and actionable data. You also need the right team to bring the clouds together in a seamless, effective, and efficient manner.
