In today’s world, with Large tech giants and businesses looking forward to moving toward serverless architecture, there has been a significant demand for scaling the applications. It’s therefore no surprise that millions of companies worldwide have adopted, or are planning on migrating to a Kubernetes and AWS Lambda solution to take their serverless applications to the next level.

Kubernetes is the leading container orchestration platform and has developed into the backbone technology for many organizations’ modern applications and infrastructure. As an open source project, “K8s” is also one of the largest success stories to ever emanate from the Cloud Native Computing Foundation (CNCF). In short, Kubernetes has revolutionized the way organizations deploy, manage, and scale applications.

Collecting and processing logs, metrics, and application data from endpoints have caused many ITOps and SecOps engineers to go gray sooner than they would have liked. Delivering observability data to its proper destination from Linux and Windows machines, apps, or microservices is way more difficult than it needs to be. We created Cribl Edge to save the rest of that beautiful head of hair of yours.

One of the first considerations for FinOps teams trying to lower their public cloud spend is investing in long-term savings vehicles available from their Cloud Service Provider. These programs can provide customers with upwards of 72% savings off on-demand prices, in return for a 1-to-3-year usage commitment, so it’s pretty common that we see them in use by our customers.

In part I of this blog series, we understood that monitoring a Kubernetes cluster is a challenge that we can overcome if we use the right tools. We also understood that the default Kubernetes dashboard allows us to monitor the different resources running inside our cluster, but it is very basic. We suggested some tools and platforms like cAdvisor, Kube-state-metrics, Prometheus, Grafana, Kubewatch, Jaeger, and MetricFire.

In our extensive guide of best ci/cd practices we included a dedicated section for database migrations and why they should be completely automated and given the same attention as application deployments. We explained the theory behind automatic database migrations, but never had the opportunity to talk about the actual tools and give some examples on how database migrations should be handled by a well disciplined software team.