There are several advantage of running services across multiple clouds. Many organization don't consider this initially due to added complexity, effort and management cost. Is there a way to have a cake and eat it too?
As we contemplated building the SaaS platform, we divided the requirements into 5 packages. If you are building a SaaS from scratch, you will likely need all the layers.
We also understand that some of you may have already started but are actively looking to accelerate your SaaS journey and scale with your current team better. Hence, we have divided the platform into 5 packages to make it easy to integrate and enable/disable specific pieces:
I was among the authors at AWS who pioneered the first SaaS control plane back in 2009, laying the groundwork for many SaaS offerings both within and beyond AWS. Crafting the SaaS control plane, we delved deep into fundamental questions:
We witness the proliferation of SaaS solutions daily — from AI applications like Private ChatGPT and infrastructure services like Snowflake, to content management systems such as WordPress, and HR management systems like Workday. The spectrum extends to customer support tools like Zendesk, customer engagement platforms like Salesforce, and collaboration solutions like Zoom, Slack, and Google Workspace. SaaS has become the prevailing distribution model in today’s software landscape and is poised to define the future of software delivery.
Software as a Service, commonly known as SaaS, is more than just a tech buzzword. It has reinvented the software ecosystem and redefined the user experience. Let’s delve deeper into why the SaaS model is a game-changer, elucidating each point with detailed explanations and real-world examples.
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
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 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.