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Readers of this blog may remember a post we made in January about Bank-Vaults that touched on the topic of disaster recovery with multi datacenter replication. In that post we dicussed replication, mostly in the context of it being used as a form of hot backup. Today we'll be exploring cold backups, another but equally important form of disaster recovery. Why we use Velero We use a toolset for Kubernetes disaster recovery called Velero.
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One of the challenges we repeatedly faced when using microservices-based solutions was how best to properly secure communication between participating services. One option was to manage security at the application layer, which meant implementing specific authentication mechanisms in the application code itself. This approach, however, would quickly become burdensome, eating up time for developers, who should be concentrating on implementing actual business logic. Wouldn't it be awesome, we thought, if developers never had to worry about implementing authentication mechanisms in their application code, and, instead, there was a magical solution that would provide secure communication between their services?
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Without a doubt Prometheus has become the de facto standard monitoring solution for Kubernetes, the same way it has become a core component of the Pipeline platform's monitoring service. However, Prometheus already has a well defined mission with a focus on alerts and the storage of recent metrics. Prometheus’ local storage is limited by single nodes in its scalability and durability. Instead of trying to solve clustered storage in Prometheus itself, Prometheus has a set of interfaces that allow integration through remote storage systems.
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Apache Kafka is a distributed streaming platform used to build reliable, scalable and high-throughput real-time streaming systems. Its capabilities, while impressive, can be further improved through the addition of Kubernetes. Accordingly, we've built an open-source Kafka operator and Supertubes to run and seamlessly operate Kafka on Kubernetes through its various features, like fine-grain broker configuration, metrics based scaling with rebalancing, rack awareness, and graceful rolling upgrades - just to name a few.
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Today we are happy to announce the 1.1 release of Backyards, Banzai Cloud's automated and operationalized service mesh product built on Istio. This is an announcement post describing the new features of Backyards. If you're not familiar with Backyards yet, and want to know why we decided to build this product, we suggest reading the blog post about the first major release. Check out Backyards in action on your own clusters:
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Kubernetes is a highly extensible framework that is built from a bunch of loosely coupled components. This gives a very high level of flexibility, but adds some new challenges to the operation compared to monolithic solutions of similar systems of the past. One of these challenges is observability, especially log collection. This post describes how to collect the logs of Kubernetes components in detail, but does not discuss the collection of application (workload) logs.
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In today's post, we'll be discussing multi-datacenter Vault clusters that span multiple regions. Most enterprises follow different replication strategies to provide scalable and highly-available services. One common replication/disaster recovery strategy for distributed applications is to have a hot standby replica of the very same deployment already setup in a secondary data center. When a catastrophic event occurs in the primary data center, all traffic is then redirected to the secondary datacenter.
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In Kubernetes clusters, the number of Operators and their managed CRDs is constantly increasing. As the complexity of these systems grows, so does the demand for competent user interfaces and flexible APIs. At Banzai Cloud we write lots of operators (e.g. Vault, Istio, Logging, Kafka, HPA, etc) and we believe that whatever system you're working with, whether it's a service mesh, a distributed logging system or a centralized message broker operated through CRDs, you will eventually find yourself in need of enhanced observability and more flexible management capabilities.
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Two months ago we announced the release of Backyards, Banzai Cloud's multi- and hybrid-cloud enabled service mesh built on top of our Istio operator. One of Backyards’ hallmarks is its ability to simplify building a production-ready Istio deployment down to a single command: backyards install -a - complete with enterprise grade security, monitoring, tracing, logs, audit, and features like canary releases, traffic management, circuit breaking and lots more, either through a convenient UI, CLI or a GraphQL API.
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A while ago we published some benchmarks and sizing about our experience of running Apache Kafka over a service mesh with the Banzai Cloud Kafka and Istio operator, orchestrated by our automated and operationalized service mesh, Backyards. The reasons for such a setup were many, and there are more details in the Running Apache Kafka over Istio - benchmark post, but let me recap some of our initial reasons, and how we evolved from there.
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