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Many organizations are embracing a more decentralized decision making process, allowing them to better build cloud native applications, and to deliver them more rapidly and safely. Often, different teams manage different microservices, with each team able to release their microservices independent of the others. Istio is exceedingly helpful for this, since it provides low level contructs able to enforce policies in the network and configure traffic control, observability, rate limiting, circuit breaking and programmable rollouts.

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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest Istio versions as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.2 support for the Banzai Cloud Istio operator. When we added Istio 1.1 support for the operator, we wrote a detailed blog post about how to employ a seamless Istio control plane upgrade in a single-mesh, single-cluster setup.

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If you’re reading this post, you’re likely already familiar with our container management platform, Pipeline, and our CNCF certified Kubernetes distribution, PKE: you probably already know how we make it possible to spin up clusters across five cloud providers and on-premise, in multi-cloud but also hybrid-cloud environments. But whether these are single or multi-cluster topologies, resilience is key. We at Banzai Cloud believe this is the case not just for infrastructural components but for entire managed application environments, like Apache Kafka.

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At Banzai Cloud we work on a multi- and hybrid-cloud container management platform, Pipeline. As a result, we’ve opensourced quite a few Kubernetes operators. While writing some of the more complex operators, such as those for Istio, Vault or Kafka, we were faced with lots of unnecessary Kubernetes object updates. These updates are a byproduct of the fact that operators are typically used to manage a large number of resources.

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In the last few months we wrote a lot of different blog posts about the Istio service mesh. We started with a simple Istio operator, then went on with different multi-cluster service mesh topologies, Istio CNI and a telemetry deep dive. The contents of the posts were built around our open source Istio operator that helps installing and managing an Istio service mesh in a single or multi and hybrid-cluster setup.

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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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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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Hybrid- and multi-cloud are quickly becoming the new norm for enterprises, just as service mesh is becoming essential to the cloud native computing environment. From the very beginning, the Pipeline platform has supported multiple cloud providers and wiring them together at multiple levels (cluster, deployments and services) was always one of the primary goals. We supported setting up multi-cluster service meshes from the first release of our open source Istio operator.

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When we added support for Istio’s service mesh in the Pipeline platform, we experienced first hand how the deployment and management of Istio can become increasingly complex. We realized that we weren’t the only ones managing Istio with Helm dealing with these problems - that demand was emerging for an Istio operator (e.g. #9333). We decided to build an Istio operator of our own, and more than a month ago we open sourced it.

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