In today's fast-paced world of cloud computing, the importance of full lifecycle management in cloud infrastructure cannot be overstated. Many organizations are quick to provision resources in cloud platforms like AWS, GCP, and Azure, but all too often, the critical decommissioning phase is overlooked. This oversight carries both financial and security implications that can be detrimental to a company's bottom line and data integrity.

The cloud landscape has evolved significantly in recent years, moving businesses beyond the confines of single cloud providers. Enterprises are increasingly embracing multi-cloud strategies to leverage the strengths of various cloud platforms. As they journey into this multi-cloud world, the intricacies of managing their infrastructure through Infrastructure as Code (IaC) become apparent, especially in the context of Software as a Service (SaaS) platforms operating in multi-tenant environments. In this blog post, we will explore the rise of multi-cloud deployments, the role of IaC as the glue that holds it together, the unique challenges posed by SaaS in a multi-cloud world, and best practices and solutions to navigate these complex scenarios.

One of the most challenging aspects of building a distributed system is scaling it to handle different levels of user traffic. In this blog post, I will discuss some of the common techniques and trade-offs involved in scaling a distributed system from 1 to 1 billion users. I will also provide some step-by-step explanations for each scale.

1. Scaling from 0–1K,
   
2. Scaling from 1K -10K,
3. Scaling from 10K-100K,
4. Scaling from 100K-1M,
5. Scaling from 1M to 1B users

Hello, fellow developers! In this blog post, I want to share with you some of the best practices and lessons learned from scaling distributed systems. Distributed systems are systems that consist of multiple independent components that communicate and coordinate with each other over a network. They are often used to handle large-scale and complex problems that require high availability, scalability, and performance.

Scaling distributed systems is an art that requires creativity, experimentation and learning. In this blog post, I will share some of the best practices and lessons learned from my experience of building and scaling distributed systems

Some of the topics I will cover are:

• How to design for scalability and reliability

• How to choose the right tools and technologies

• How to monitor and troubleshoot distributed systems

• How to handle failures and recoveries

I hope you will find this blog post useful and inspiring for your own scaling journey

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.