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A few weeks ago we announced a new version of Pipeline, the hybrid any-cloud platform. This post is part of a series of posts highlighting the multi- and hybrid-cloud features on that platform. Today, we will be focusing specifically on multi-cloud features. Before we take a deep dive into our technical content, let’s go over some of the key expectations an enterprise has when it embraces a multi-cloud strategy:

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Amid a growing number of increasingly sophisticated cyber attacks, enterprises are searching for ways to enable security wherever possible, in order to protect their data in transit and at rest. Big data processing is no exception; security is a very broad topic and to cover it in its entirety would be beyond the scope of this post. Instead, we will focus exclusively on those security capabilities that Spark on Kubernetes provides (by Spark on Kubernetes, we mean when Spark uses Kubernetes as an external cluster manager for creating and running executors).

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Service mesh probably needs no introduction. But, just to recap, let’s define it as a highly configurable, dedicated and low‑latency infrastructure layer designed to handle and provide reliable service-to-service communication, implemented as lightweight network proxies deployed alongside application code. Typical examples of mesh services are service discovery, load balancing, encryption, observability (metrics and traces) and security (authn and authz). Circuit breakers, service versioning, and canary releases are frequent use cases, all of which are part of any modern cloud-native microservice architecture.

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Yes, we opensourced yet another Apache Kafka operator for Kubernetes. This might seem bizarre, considering the alternatives that are already available (they exist but there are not too many), so you may be wondering, ‘Why?’ Well, keep reading and we’ll tell you: from design gaps and features we believe are necessary to operate Kafka on K8s, through my personal fix for Envoy, to some of our specific usage scenarios.

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There has been a lot of talk about multi- and hybrid-cloud deployments over the past years. Some cloud vendors see these trends as a threat, others look at them as an opportunity. We think that beneath the buzzwords lie some very important use-cases driven by the needs of enterprises and SaaS providers. However, delivering and operating multi- and hybrid-clouds had been too complex for most organizations so far. The use-cases that we’ve seen at customers broadly relate to three main areas: flexibility, cost optimization, and compliance.

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The Cloudinfo project has been a core part of the Pipeline platform since day one. We built and open sourced Cloudinfo as a unified interface that would allow us to access prices and services from all the cloud providers we support (AWS, Azure, Google, Alibaba and Oracle), resulting in a multi- and hybrid-cloud application platform. When a Pipeline user creates a single, hybrid- or multi-cloud Kubernetes cluster in one of the cloud providers or on-premise, they have the option of choosing between specifying their resource requirements (number of CPUs, memory, advanced network, I/O, etc) or simply selecting the cheapest cloud provider.

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One of the core features of the Istio service mesh is the observability of network traffic. Because all service-to-service communication is going through Envoy proxies, and Istio’s control plane is able to gather logs and metrics from these proxies, the service mesh can give you deep insights about your network. While a basic Istio installation is able to set up all the components needed to collect telemetry from the mesh, it’s not easy to understand how these components fit together and how to configure them in a production environment.

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The following is a guest blog post from Jürgen Weber, Bank-Vaults user and contributor extraordinaire. Here at hipages, we have a legacy approach to how we keep and maintain our ‘secrets’. The login details for some of our primary application resources are easy to obtain and with this carries great risk.. So to solve this we decided to embark on a ‘secrets’ project and implement Hashicorps Vault. As a part of this project, we looked at a variety of solutions.

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Every major cloud provider offers a managed Kubernetes service that aims to simplify the provisioning of Kubernetes clusters in its respective environment. The Banzai Cloud Pipeline platform has always supported these major providers - AWS, Azure, Google, Oracle, Alibaba Cloud - turning their managed k8s services into a single solution-oriented application platform that allows enterprises to develop, deploy and securely scale container-based applications in multi-cloud environments. While this was very appealing from the outset, we quickly realized there was demand among our enterprise users to implement more sophisticated use cases that were limited by our initial approach.

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Logs (one of the three pillars of observability besides metrics and traces) are an indispensable part of any distributed application. Whether we run these applications on Kubernetes or not, logs are one of the best ways to diagnose and verify an application state. One of the key features of our Kubernetes platform, Pipeline, is to provide out-of-the-box metrics, trace support and log collection. This post highlights some of the behind the scenes automation we’ve constructed in order to achieve this.

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