Operations | Monitoring | ITSM | DevOps | Cloud

Back in December, Amazon Web Services (AWS) and Grafana Labs partnered to launch the Amazon Managed Service for Grafana in a preview to a limited set of customers. Amazon Managed Service for Grafana is a scalable managed offering that provides AWS customers a native way to run Grafana directly within AWS alongside all their other AWS services.

Docker v20 was officially released a few months ago, bringing new features that will make your builds faster and more secure. We will be progressively rolling out the new version to Bitbucket Pipelines throughout the month of June 2021.

The race to cloud migration can seem like a good idea for most businesses, but a common trend has been to get there as soon as possible. Much like with the story of the tortoise and the hare, there are lessons to be learned from moving too fast. When it comes to cloud migration, businesses would be smart to choose a more strategic route and take their time.

If you’re looking for a way to drive conversion rates up and bounce rates down, attract new customers, and make your existing client base more loyal—and who isn’t?—you’ve got to invest in improving the user experience of your apps and services.

At the end of last week, a significant BGP leak caused widespread network outages that impacted major network operators, cloud, and CDN providers. The incident on Friday, April 16th, 2021 was (yet another) classic origin hijack case from Vodafone Idea (AS55410), an Indian operator based in Mumbai and Gandhinagar. The Vodafone Idea ASN was inundated with traffic,13 times higher than average, leaving its users unable to access the internet.

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.

A common challenge in data engineering is to combine traditional data warehousing and BI reporting with experiment-driven machine learning projects. Many data scientists tend to work more with Python and ML frameworks rather than SQL. Therefore, their data needs are often different from those of data analysts. In this article, we’ll explore why having a data lake often provides tremendous help for data science use cases.

It’s April, and that means it’s Mathematics and Statistic Awareness month. And in our everyday world of monitoring and observability, both play an ever-increasing role in how we keep track of our environments, both our apps and our infrastructure. Our world is no longer about just pinging the server/app to make sure “It’s alive!”.

Telegraf is a server-based agent for collecting all kinds of metrics for further processing. It’s a piece of software that you can install anywhere in your infrastructure and it will read metrics from specified sources – typically application logs, events, or data outputs.

It was revealed just a few days ago that US Federal investigators are looking into an intrusion and insertion of malicious code into Codecov. As many readers here will already know, Codecov is a software auditing tool that analyses your source code to check for the amount of test coverage. The intrusion targeted the Codecov bash uploader, which is a script that provides a way to send coverage reports to Codecov.