Category: Advanced (300)

Customers have different reasons to run multiple Red Hat OpenShift clusters, including having separate clusters per geographical locations, setting a cluster-level boundary between mission-critical applications, data residency, and reducing latency for end users. This post explores Red Hat Advanced Cluster Management for Kubernetes and how it extends the value of Red Hat OpenShift for hybrid environments. It also explores different scenarios where having a multi-cluster environment is beneficial.

Learn how to build a Java application in a Docker container and push the container image to HAQM ECR orchestrated by AWS CodePipeline. We’ll use Snyk to scan your code, build a container image, and display the results in both Snyk and HAQM ECR. We’ll also show you how HAQM Inspector utilizes Snyk open source to provide insight into your software security vulnerabilities. All of this functionality is available from the AWS Management Console.

Open banking is revolutionizing financial services and companies like Yapily are at the forefront of this new wave of innovation. Learn how to build and deploy a basic application on AWS in a few simple steps using the Yapily Bank Connector. We’ll demonstrate how an end user would select their bank from a list, securely connect to that bank and give consent to either share data or make a payment. Connecting to thousands of banks, Yapily uses a secure open API to power industry leaders in financial services.

AWS Transfer Family provides SFTP, FTPS, and/or FTP access to HAQM S3 or HAQM HAQM EFS. It does that by providing a secure, highly available, and scalable server endpoint. You pay for the time this endpoint is enabled, and for data transfer (upload and download). Learn how this makes a multi-tenant setup where an endpoint is shared between different users (or applications), a more cost-efficient solution than having dedicated endpoints.

Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to HAQM EKS.

Learn how T-Systems has enabled AWS Config at scale on Deutsche Telekom IT’s AWS landing zone, which is a brownfield environment with more than 600 AWS accounts. AWS Config is a governance and compliance service that enables you to assess, audit, and evaluate the configurations of your resources. The main motivator for Deutsche Telekom IT using AWS Config is that developing compliance checks using custom tools was time consuming, unscalable, and came with a large operational overhead.

As part of its SaaS platform, Druva runs containerized applications with microservices architecture using Auto Scaling groups and HAQM HAQM ECS. Explore the cost optimization journey of Druva using Spot instances with Auto Scaling groups, challenges faced by them during this journey, and how attribute-based instance type selection (ABS) came to the rescue in addressing those challenges in optimizing costs.

The F5 BIG-IP Virtual Edition (VE) load balancer deployment adds new Layer 4 application capabilities and added visibility to those applications inside an HAQM EKS cluster to ensure a successful deployment in a containerized environment. This post presents a step-by-step guide for using the F5 BIG-IP VE on AWS as a load balancer for EKS clusters by using additional components, including the F5 Container Ingress Service (CIS) and F5 IPAM Controller (FIC).

Data-driven decision making is accelerating and defining the way organizations work. With this transformation, there has been a rapid adoption of data lakes across the industry. Hear from Rackspace, an AWS Premier Tier Services Partner, about the drawbacks of existing data lake architecture, what Apache Iceberg is, and how it overcomes the shortcomings of the current state of data lakes. Then dive deep on the design differences between Apache Hive and Iceberg.

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and HAQM SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.