In today’s fast-paced world, applications are vital for driving businesses forward. However, without proper monitoring and insights into your application’s performance, you can’t identify what causes slow response times, high CPU usage, or database bottlenecks. But with an Application Performance Monitoring (APM) tool, you can gain deep visibility into your application’s performance by tracking critical metrics.

Amazon Athena is heralded for its simplicity, pay-per-query pricing, and speed. Others say Athena has its fair share of limitations, which could affect your overall analytics experience and quality of analyses. So, what is what? In this post, we’ll share what Amazon Athena does, compare it to Amazon Redshift, and share how to understand and control your Athena costs in AWS (with just one tool).

In a recent user group meeting, guest speaker Marc Luescher from Amazon Web Services (AWS) joined us to give an overview of Amazon Security Lake. We talked about Cribl use cases and how Cribl Stream can bring your non-AWS data into the Security Lake. Enterprises are dealing with some significant challenges with security data in 2023. Inconsistent, incomplete, poorly-formatted log data is simultaneously scattered across companies and locked up in different silos within the organization.

Cloud deployments and containerization let you provision infrastructure as needed, meaning your applications can grow in scope and complexity. The results can be impressive, but the ability to expand quickly and easily makes it harder to keep track of your system as it develops. In this type of Kubernetes deployment, it’s essential to track your containers to understand what they’re doing.

Even the best software fails sometimes. How quickly those failures get addressed, and how your teammates and customers feel about you after the fact, comes down to how well you communicate with them. Users, customer success managers, Ops team members, IT, security, engineering leadership, even the executive team. Each has a vested interest in resolving engineering incidents quickly. All need to be updated with the right information at the right time.

Curious about continuous deployment vs delivery? We explain what each is, what happens in each step, and their importance in the DevOps lifecycle.

We’re excited to announce an important extension to our API. After rigorous research, testing, and feedback, we’ve launched the Error Groups endpoints. This powerful new addition aims to enhance your monitoring experience and better handle errors across your applications. Our primary aim with the introduction of these endpoints is to offer you a more detailed view into your applications’ error groups, and to offer a way to update their statuses using the API.

Kubernetes, the de-facto standard for container orchestration, supports two deployment options: imperative and declarative. Because they are more conducive to automation, declarative deployments are typically considered better than imperative. A declarative paradigm involves: The issue with the declarative approach is that YAML manifest files are static.

In this article, learn how to setup application monitoring for Python apps using an open-source solution, SigNoz.

At first glance, this may appear counterintuitive. After all, aren’t public clouds lauded for their scalability, flexibility, and cost-efficiency? However, a closer examination reveals a more nuanced reality. This article delves into the driving forces behind cloud repatriation, helping you determine whether it might be the right fit for your organization.