As the world of technology continues to evolve, containerization has become a popular choice for deploying applications. Kubernetes is an open-source container orchestration system that has gained popularity due to its ability to manage and deploy containers across multiple hosts.

However, managing Kubernetes and container networking in multi-cloud environments can be challenging. This is where Sparta-like skills can come in handy.

Service Level Agreements (SLAs), Service Level Indicators (SLIs), and Service Level Objectives (SLOs) are critical metrics for measuring the performance and reliability of IT services. These metrics provide valuable insights into the quality of service provided to customers and help teams identify areas for improvement. In this blog post, we’ll provide a step-by-step guide to calculating SLAs, SLIs, and SLOs for your IT services, using an example of a microservices-based ecommerce application.

Are you tired of juggling multiple cloud providers for your Open Search needs? Do you feel like you're drowning in a sea of APIs and configurations? -- this guide will help you navigate the stormy waters of managing Open Search across multiple clouds.

First, let's talk about the challenges you may face.

One of the biggest challenges is dealing with different API endpoints and configurations across different cloud providers. For example, AWS Open Search uses a different API endpoint than Azure Open Search, and each provider has its own set of configuration options.

Kubernetes has become the go-to container orchestration tool for many organizations. However, achieving the full potential of Kubernetes requires the right operating system. Kubernetes is a popular open-source container orchestration system for automating the deployment, scaling, and management of containerized applications.

Amazon Linux 2023, the latest version of Amazon Linux, is optimized for running workloads on AWS, and it provides a powerful platform for running Kubernetes clusters.

In this blog post, we will discuss how Amazon Linux 2023 can help you unlock the full potential of Kubernetes, with code examples that showcase the advanced features and capabilities of the operating system.

AKS Edge Essentials, an on-premises Kubernetes implementation of Azure Kubernetes Service that automates running containerized applications at scale on PC-class or “light” edge hardware. It highlights the features, benefits and use cases of AKS Edge Essentials, such as:

• A lightweight and supported Kubernetes distribution with a simple installation experience
• A cloud-based management plane for Kubernetes clusters running anywhere
• Support for both Linux-based and Windows-based containers
• Interoperability between native Windows applications and containerized Linux workloads
• A fully supported stack from kernel to cloud with security and update policies
• Azure Arc integration to extend the Azure platform to the edge with core services

Kubernetes-based event-driven autoscaling (KEDA) is a powerful tool for automating the scaling of your Kubernetes applications based on event-driven workloads. KEDA is an open-source project that is built on top of Kubernetes, and it allows you to scale your workloads dynamically based on the volume of events that are generated by your applications. In this blog post, we’ll provide an introduction to KEDA and show you how to get started using it with examples in Java, Golang, and YAML code.

Kubernetes is a popular open-source platform for managing containerized applications across multiple nodes and clusters. It provides features such as service discovery, load balancing, orchestration, scaling, and self-healing. However, running Kubernetes across different cloud providers, such as AWS, Azure, Google Cloud, etc., can pose some challenges and complexities, such as network connectivity, resource synchronization, and cost optimization.

In this blog post, we will explore how to achieve autoscaling in multi-cloud Kubernetes deployments, which can help us improve the performance, availability, and efficiency of our applications. We will also show some code examples of how to configure and deploy autoscaling policies and parameters for each cluster and cloud provider.

In our last post, we talked about the emergence of the Cloud and we feel this is where the Cloud model went wrong.

As the Cloud grew, Cloud providers figured out a way to monetize open-source technologies by starting hosting them in the cloud. The challenge with this model is that open-source technology providers are left with all the hard work to build and maintain their projects but not with much benefits.

Speed ​​is an important factor for business in this era when customers are looking for instant gratification. If the fact doesn’t convince you, the statistics might. An element as simple as a website’s load time carries weight. Statistics indicate that the first five seconds of a page load time have the greatest impact on conversion rates. So when consumer behavior has the greatest impact, it is profitable in the long run to modernize a business model accordingly.

The faster a company can develop and ship a product to its customers, the more likely it is to avoid problems in a fast-paced environment. Cloud Native as a form of technology is designed for this. It is a behavior-driven development model designed, built, and optimized to run in the cloud.

Cloud native applications can easily be mistaken as another tool for the digital first era or another platform. However, it is a complete shift to a set of different practices, automated testing, design, customer centric model and an accelerated production environment. With shorter delivery cycles and higher quality, working in the cloud native database requires a transformation within the entire development team of an organization.

Before 2005, most of the technology was open source and enterprises were consuming open source to accomplish their business goals and the trend seems to continue to grow. Take the operating system as an example. The use and popularity of Linux have been growing day by day. Another good example is the database industry where MySQL and Postgres are rapidly growing over time as opposed to Oracle or Microsoft’s SQL Server.

The open-source trend was not surprising as proprietary technology leads to lock-ins and as these enterprises grow, their reliance on these foundational pieces continues to grow over time. This makes it really difficult and costly for these enterprises to switch to new technology, locking them into the closed-source technology and leaving them vulnerable to hefty prices or punitive charges as they scale. This drove many enterprises to come together and join hands to collectively build different open-source components.