KEDA 2.0 – An update from KEDA 1.0

KEDA 2.0 – An update from KEDA 1.0

In a previous blog post – Serverless anywhere with Kubernetes – I wrote how you could write serverless code and run it in a container wherever you want without any vendor lock-in.

KEDA (Kubernetes-based Event-Driven Autoscaling) is an opensource project built by Microsoft in collaboration with Red Hat, which provides event-driven autoscaling to containers running on an AKS (Azure Kubernetes Service), EKS ( Elastic Kubernetes Service), GKE (Google Kubernetes Engine) or on-premises Kubernetes clusters 

KEDA allows for fine-grained autoscaling (including to/from zero) for event-driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource definition. Most of the time, we scale systems (manually or automatically) using some metrics that get triggered.

For example, if CPU > 60% for 5 minutes, scale our app service out to a second instance. By the time we’ve raised the trigger and completed the scale-out, the burst of traffic/events has passed. KEDA, on the other hand, exposes rich events data like Azure Message Queue length to the horizontal pod auto-scaler so that it can manage the scale-out for us. Once one or more pods have been deployed to meet the event demands, events (or messages) can be consumed directly from the source, which in our example is the Azure Queue.

Quote from Serverless anywhere with Kubernetes
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Password-less Authentication with FIDO2 in Azure or other Microsoft services

Password-less Authentication with FIDO2 in Azure or other Microsoft services

As a security-conscious person, I try all the time to find new ways to enhance my security posture in ways where I get the best of both worlds without adding too much complexity. That means that I want to have the latest and greatest security measures implemented without having a very complex infrastructure which, in the end, it might end up as a single point of failure. 

In some ways, you might consider me more of a paranoid person rather than a security-conscious person. My whole home has layers over security layers implemented where you might say that I harbor military secrets.

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Governance & Security in Azure – The untold truth

Moving to the cloud is “easy”; Managing it is another ordeal.

I’m going to start with a disclaimer; This post focuses on achieving governance and security in Azure; This doesn’t mean that what I’m going to write here cannot apply to AWS or Google, they have other technologies that can help you achieve full governance;

Let’s continue.

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Azure SQL Databases – DTU or vCore

Azure SQL Databases – DTU or vCore

DTU or vCore? That is the question.

When they first came as an offering a long time ago, Azure SQL Databases always had a mystery attached to them regarding the performance tier you should use.

DTU

Database Throughput Unit or DTU is a way to describe the relative capacity of a performance SKU of Basic, Standard, and Premium databases. DTUs are based on a measure of CPU, memory, I/O reads, and writes. When you want to increase the “power” a database, you just increase the number of DTUs that are allocated to that database. A 100 DTU DB is much more powerful than a 50 DTU one.

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Serverless anywhere with Kubernetes

Serverless anywhere with Kubernetes

KEDA (Kubernetes-based Event-Driven Autoscaling) is an opensource project built by Microsoft in collaboration with Red Hat, which provides event-driven autoscaling to containers running on an AKS (Azure Kubernetes Service), EKS ( Elastic Kubernetes Service), GKE (Google Kubernetes Engine) or on-premises Kubernetes clusters 😉

KEDA allows for fine-grained autoscaling (including to/from zero) for event-driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource definition. Most of the time, we scale systems (manually or automatically) using some metrics that get triggered.

For example, if CPU > 60% for 5 minutes, scale our app service out to a second instance. By the time we’ve raised the trigger and completed the scale-out, the burst of traffic/events has passed. KEDA, on the other hand, exposes rich events data like Azure Message Queue length to the horizontal pod auto-scaler so that it can manage the scale-out for us. Once one or more pods have been deployed to meet the event demands, events (or messages) can be consumed directly from the source, which in our example is the Azure Queue.

CONTINUE READING

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