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An Introduction to Apache Airflow and Talend: Orchestrate your Containerized Data Integration and Big Data Jobs

In my last blog I described how to achieve continuous integration, delivery and deployment of Talend Jobs into Docker containers with Maven and Jenkins. This is a good start for reliably building your containerized jobs, but the journey doesn't end there. The next step to go further with containerized jobs is scheduling, orchestrating and monitoring them.

Steps to Deploying Kong as a Service Mesh

In a previous post, we explained how the team at Kong thinks of the term “service mesh.” In this post, we’ll start digging into the workings of Kong deployed as a mesh. We’ll talk about a hypothetical example of the smallest possible deployment of a mesh, with two services talking to each other via two Kong instances – one local to each service.

Microservices and Service Mesh

The service mesh deployment architecture is quickly gaining popularity in the industry. In the strategy, remote procedure calls (RPCs) from one service to another inside of your infrastructure pass through two proxies, one co-located with the originating service, and one at the destination. The local proxy is able to perform a load-balancing role and make decisions about which remote service instance to communicate with, while the remote proxy is able to vet incoming traffic.

Sharpening the Axe: Our Journey into Disruption with Kong

Jason Walker shares how Cargill is using Kong to transform legacy architecture with a “Cloud first, but not always” approach. Hear why Cargill chose Kong for their API gateway as part of their internal API platform, Capricorn, allowing Jason’s small team to stay nimble while they administer decentralized deployments. In this talk from Kong Summit 2018, Jason shares how Kong routes traffic in Cargill’s Kubernetes cluster.