Managing projects isn’t an easy task, particularly if you’re managing parallel projects with inter-team dependencies. Lack of visibility coupled with difficulty in obtaining the right metrics on time can make it nearly impossible for project teams to track issues, action items, and risks, often resulting in projects running behind schedule, overshooting budgets, or worse, getting stalled due to unforeseen problems.

I’ve always had a good experience using DigitalOcean, a cloud infrastructure provider which offers developers cloud services that help deploy and scale applications that run simultaneously on multiple computers. I’ve used DigitalOcean a lot for my personal projects — for example, to host my personal blog, its stats, and a NextCloud instance, all running in Kubernetes.

Currently, there is no official InfluxDB C language client library. Fortunately, I wanted to do exactly that for capturing Operating System performance statistics for AIX and Linux. This data capturing tool is called “njmon” and is open source on Sourceforge. So having worked out how and developing a small library of 12 functions for my use to make saving data simple, I thought I would share it. I hope it will prove useful for others.

It’s 2021 and somehow, I’m still trying to convince people that content is one of the biggest revenue drivers there is. Back in 2017, there was an influx of “content is king” and “why content is important” blogs on LinkedIn and Medium, but it seems little notice has been taken in the years that have followed.

The cold tier of searchable snapshots, previously beta in Elasticsearch 7.10, is now generally available in Elasticsearch 7.11. This new data tier reduces your cluster storage by up to 50% over the warm tier while maintaining the same level of reliability and redundancy as your hot and warm tiers.

This article is a re-post of the article written by Matthew Gregory and published on the Ockam blog. Let’s investigate how to build applications with trusted time series data in a zero trust environment! To trust an application we need to trust the data that feeds into it. Increasingly, applications rely on time series data from outside the datacenter, at the edge, or in IoT. This means we need to think of trust and data in new ways.

Link Analysis is a data analysis approach used to discover relationships and connections between data elements and entities. This is a very visual and interactive technique that can be done in the Splunk platform – and is almost always driven by a person, an analyst or investigator, to understand the data and discover necessary insights specific to the business problem at hand.

One of the great things about InfluxDB is that it is really easy to get up and running, and it doesn’t require much monitoring when you are dealing with datasets that fit well on your local dev machine. Once you start using InfluxDB in production and pushing orders of magnitude more data into the system, it’s critical to monitor how your instance is performing so that you can proactively respond to things like disk or network failures, memory saturation, and write or query loads.

Here at InfluxData, we’ve been focusing recently on deepening our support for Microsoft Azure. First we turned on InfluxDB Cloud on Azure West Europe, in Amsterdam, back in July. Then we launched InfluxDB Cloud on Azure East US, in Virginia, in September. Today, we’re pleased to announce that InfluxDB Cloud joins InfluxDB Enterprise on Azure Marketplace.