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Use AWS Controllers for Kubernetes To Deploy a Serverless Data Processing Solution With SQS, Lambda, and DynamoDB

In this blog post, you will be using AWS Controllers for Kubernetes on an Amazon EKS cluster to put together a solution wherein data from an Amazon SQS queue is processed by an AWS Lambda function and persisted to a DynamoDB table.

AWS Controllers for Kubernetes (also known as ACK) leverage Kubernetes Custom Resource and Custom Resource Definitions and give you the ability to manage and use AWS services directly from Kubernetes without needing to define resources outside of the cluster. The idea behind ACK is to enable Kubernetes users to describe the desired state of AWS resources using the Kubernetes API and configuration language. ACK will then take care of provisioning and managing the AWS resources to match the desired state. This is achieved by using Service controllers that are responsible for managing the lifecycle of a particular AWS service. Each ACK service controller is packaged into a separate container image that is published in a public repository corresponding to an individual ACK service controller.

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Write Your Kubernetes Infrastructure as Go Code – Combine CDK8s With AWS CDK

In an earlier blog post you saw how to use cdk8s with AWS Controllers for Kubernetes (also known as ACK), thanks to the fact that you can import existing Kubernetes Custom Resource Definitions using cdk8s! This made it possible to deploy DynamoDB along with a client application, by using cdk8s and Kubernetes.

But, what if you continue using AWS CDK for AWS infrastructure and harness the power cdk8s (and cdk8s-plus!) to define Kubernetes resources using regular code? Thanks to the native integration between the AWS EKS module and cdk8s, you can have the best of both worlds!

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Write Your Kubernetes Infrastructure as Go Code - Manage AWS Services

AWS Controllers for Kubernetes (also known as ACK) is built around the Kubernetes extension concepts of Custom Resource and Custom Resource Definitions. You can use ACK to define and use AWS services directly from Kubernetes. This helps you take advantage of managed AWS services for your Kubernetes applications without needing to define resources outside of the cluster.

Say you need to use an AWS S3 Bucket in your application that’s deployed to Kubernetes. Instead of using AWS console, AWS CLI, AWS CloudFormation etc., you can define the AWS S3 Bucket in a YAML manifest file and deploy it using familiar tools such as kubectl. The end goal is to allow users (Software Engineers, DevOps engineers, operators etc.) to use the same interface (Kubernetes API in this case) to describe and manage AWS services along with native Kubernetes resources such as Deployment, Service etc.

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Manage Redis on AWS From Kubernetes

In this blog post, you will learn how to use ACK with Amazon EKS for creating a Redis cluster on AWS (with Amazon MemoryDB).

AWS Controllers for Kubernetes (also known as ACK) leverage Kubernetes Custom Resource and Custom Resource Definitions and allow you to manage and use AWS services directly from Kubernetes without needing to define resources outside of the cluster. It supports many AWS services including S3, DynamoDB, MemoryDB etc.

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Enhancing a Kotlin Chart With Advanced Charting Kit (Part 2)

In part 1 of this blog series, I demonstrated how the Advanced Charting Kit can be used to enhance a shinobichart using Kotlin. In this blog post, I’d like to show you how you can improve your chart a little further. Again, I’ll demonstrate this using the heart rate chart that I used in part 1. At the end of part 1, we had a chart that looked as below:

While the chart now shows pace as well as heart rate, I feel it looks a little crowded. The relationships between the data series are not immediately obvious and I feel — to some degree — adding the pace data clutters the chart when it is viewed fully zoomed out. Rather than display all of the pace data upon the initial loading of the chart, I think this could be a great fit for ACK’s drill down animation feature. Why don’t we have the pace data appear as the user zooms in on the x or time axis?