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33 Kubernetes security tools

Kubernetes security tools … there are so freaking many of them; with different purposes, scopes and licenses. That’s why we decided to create this Kubernetes security tools list, including open source projects and commercial platforms from different vendors, to help you choose the ones that look more interesting to you and guide you in the right direction depending on your Kubernetes security needs.

How to detect Kubernetes vulnerability CVE-2019-11246 using Falco

A recent CNCF-sponsored Kubernetes security audit uncovered CVE-2019-11246, a high-severity vulnerability affecting the command-line kubectl tool. If exploited, it could lead to a directory traversal, allowing a malicious container to replace or create files on a user’s workstation. This vulnerability stemmed from an incomplete fix of a previously disclosed vulnerability (CVE-2019-1002101). Are you vulnerable?

4 Tips to Prevent Website Availability Issues

Website availability is a top priority for enterprise IT teams. Depending on the purpose of your website, even a couple of seconds of downtime can cost big money. That’s why monitoring for website availability issues is a critical part of any IT team’s overall mission. Downtime happens. But sometimes it can be prevented. We’ve put together this guide to help you get the most out of your website uptime monitor. Let’s dive in, shall we?

Release 1.16.0: Smarter binaries and built-in TLS

We’re excited to launch release v1.16.0 of the open-source Netdata monitoring agent, which delivers real-time health monitoring and performance troubleshooting to nearly any system or application. This release also contains 40 bug fixes, 31 improvements, and 20 documentation updates—if you’d like to see the full list, check out the full release notes.

Continuous Database Monitoring

Continuous Database Monitoring is a very important aspect of enterprise applications monitoring. Database is the foundation of any application. If the performance of the database is not good then every user request can be impacted. Continuous database monitoring does provide very quick ROI. Tweaking the time consuming SQLs and any other database bottlenecks have impact on performance, scalability and availability of the entire application.

What's new in Calico v3.8

We are very excited to announce Calico v3.8. Here are some highlights from the release. You can now view IP address usage for each IP pool using calicoctl. This allows you to more easily manage the IP space in your cluster, providing a simple way to see which IP pools have addresses available and which are running low. See the calicoctl reference documentation for more detailed information on how to use this feature.

How I made AWS Lambda work for my SaaS

A big part of Checkly runs on AWS Lambda, but I never really discussed it in depth before on this blog. So here we go. Topics are: Note, I'm using "Lambda" here as a stand in for "serverless" in general. Many of the things discussed here apply to either Google Cloud Functions, Azure Functions and possibly Zeit although I've never used it. First something on how we use Lambda. Last week we went over 35 million check runs.

Building Your Observability Practice with Tools that Co-exist

A lot of product marketing is about telling people to throw away what they have in favor of something entirely new. Sometimes that is the right answer–sometimes what you have has completely outlived its usefulness and you need to put something better in its place–but a lot of the time, what’s realistic is to make incremental improvements. If you’ve been tasked with starting, or growing your observability practice, it may seem a long journey from here to there.

Identifying bottlenecks and optimizing performance in a Python codebase

July 08, 2019 In this post, we will walk through various techniques that can be used to identify the performance bottlenecks in your python codebase and optimize them. The term "optimization" can apply to a broad level of metrics. But two general metrics of most interest are; CPU performance (execution time) and memory footprint. For this post, you can think of an optimized code as the one which is either able to run faster or use lesser memory or both. There are no hard and fast rules.