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Product Metrics for Discovery Activities

Most companies today compile a set of metrics for their product teams to regularly report on to the company management. This includes a variety of product performance metrics(usage frequency, churn rate, NPS, etc.). But a lot of them struggle a bit with product discovery activities. So how do your track discovery?

Using Automation and SLOs to Create Margin in your Systems

With the difficulties we’re facing during this time, it can be difficult to keep up with the increasingly vast demand for our services. You need to make use of all the tools in your toolbelt in order to conserve your team’s cognitive resources. Two ways you can do this are through automating toil from your processes and prioritizing with SLOs.

Semiannual Report of Unplanned Server Downtime | 2020 Q1 + Q2

No, this isn’t physics news; hold that Nobel Prize. This is about downtime; the dark matter of the web. It’s invisible to most of us, but its gravity has huge effects on commerce, companies, and markets. For everybody who does business online, unplanned website or service outages drag down their revenue, drag down their profits, and drag down their brand.

Azure Monitor Agents: their different functions

In the Azure Monitor Learning Path, we talked about metrics and collecting data in a Log Analytics workspace to be operated on with KQL. As a part of it, we also talked about Monitoring Solutions and how they help you collect data into the workspace that is more focused for specific purposes. In that series, our main focus was on the Log Analytics Agent and I briefly talked about Diagnostics extension.

Observability: From Push to Production

Developers are building and deploying to production with greater frequency. Elite organizations are deploying to production multiple times per day. All the while we continue to distribute our applications even wider with the adoption of micro-services, and global deployments. This consistent churn and increasing code complexity create the perfect storm that makes finding problems even harder. How do you know the changes just committed actually deployed? How do you know the changes worked?

Log aggregation and the journey to optimized logs

Ever experienced bad logging- whether it’s the wrong log, the wrong information, or a multitude of other logging woes? We aren’t able to count the number of times anymore that we’ve happily gone and set log lines, only to find out that it was all for naught. The frustrations are endless. What is meant to be magic for your code, the ultimate savior when debugging, has become the ultimate frustration.