Building upon our initial introduction of the control and data planes, this post delves deep into the interplay between these two planes. Shedding light on their individual roles and their combined impact on the overall SaaS experience. As we navigate through this post, we'll uncover the magic behind how these components work in tandem to ensure seamless operations, swift data transfers, and a scalable environment.
Scaling - it’s the reason we’re all using this cloud thing anyway, right? Surely all of your applications have been tested to effortlessly scale from 0 to 1,000 in milliseconds, and your databases can rebalance after scaling within minutes with zero impact to anything important, correct?
Don’t worry, I don’t think anyone has fully cracked this nut. But why is this? What makes it so difficult to actually get ALL the benefits of the infinitely flexible cloud?
In the first post of this two-part series, we introduced primary topics under the umbrella of cloud platform monitoring and went into a bit of detail for how they present specific challenges. In this follow-up post we’ll explore some of the state-of-the-art strategies for dealing with these issues and the additional complications that will arise when utilizing these techniques.
Most people who work in platform engineering and cloud infrastructure are aware that you need to design both your applications and your underlying platform for high availability and fault tolerance, but there is a large range of resiliency from “relatively reliable” to “bulletproof”. The common adage goes something like this; for each “additional 9” of reliability, you’ll need to spend an exponentially greater amount of effort and cost to achieve it.
Why is this? And what goes into these additional levels?
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.
In the world of computing and cloud services, the terms "control plane" and “data plane” are increasingly mentioned, even though these are actually older terms that originally came about in the networking world. This blog post aims to dive into the history of these concepts, where they are used and why, and how they’ve been extended into the emerging domain of SaaS. Let's dive in.
Certification
We are back with more exciting news. After our proud announcement of achieving the SOC 2 Type I certification, we have now achieved another significant milestone by earning the SOC 2 Type II Certification. This milestone underscores our commitment to creating a safer, more secure environment for our clients and their data.
Software as a Service (SaaS) is a popular and convenient way of delivering applications over the internet, without requiring users to install or maintain them on their own devices. SaaS providers can offer scalable, secure, and cost-effective solutions to their customers, while also benefiting from faster development cycles and lower operational overhead.
Kubernetes is an open-source platform that enables SaaS providers to automate the deployment, scaling, and management of their applications across multiple clusters of servers. Kubernetes provides a consistent and reliable environment for running SaaS applications, regardless of the underlying infrastructure or cloud provider. Kubernetes also offers features such as service discovery, load balancing, health checks, configuration management, and security policies that simplify the development and operation of SaaS applications.
The proliferation of various cloud computing services has resulted in the widespread adoption of several new concepts and vocabularies in the world of IT infrastructure. IaaS, PaaS, and SaaS are frequently given examples of these cloud services. Yet, there isn't always enough information on hand to make the distinctions among these categories of cloud deployment models very clear. This article aims to clarify these concepts, go deeper into their background, present examples, explain use cases, and emphasize their main differences.
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.