Kubernetes is one of the most significant advances in cloud computing since the public cloud. It provides an open-source, highly scalable, and cloud-native platform for orchestrating containerized applications. More organizations now use Kubernetes to deploy, network, scale, and manage containers at scale. In addition, the Kubernetes architecture empowers them to deploy containerized applications swiftly across various platforms, support distributed systems, and scale operations to meet demands quickly.

In June 2024, Amazon hit an astounding $2 trillion valuation, joining the elite ranks of companies like Alphabet, Apple, Microsoft, and Nvidia. This growth was largely driven by the success of its cloud computing division, Amazon Web Services (AWS), which holds one-third of the global cloud market share. That equals the combined market share of Microsoft Azure and Google Cloud! But who are AWS’s biggest contributors to this success?

We see it often. Too many companies struggle to optimize their cloud costs — whichever platform they use, from Microsoft Azure to Amazon Web Services (AWS) and Google Cloud Platform (GCP). So, in this guide, we are sharing over 20 Azure cost optimization best practices you can apply right away — all in a calorie-free, snackable guide you can read in less than a coffee break.

In 2006, Amazon Web Services (AWS) pioneered cloud computing as we know it today. With about 34% of the market share, Amazon Web Services remains the largest cloud infrastructure provider today, followed by Microsoft Azure and Google Cloud Platform (GCP). AWS is a Cloud Service Provider (CSP). That means AWS builds, maintains, and continually improves multiple data centers worldwide and then rents the computing infrastructure to other companies over the Internet as needed.

If your gross margin is lower than 60-90%, you have a weaker SaaS margin than you’d want. This margin can turn away investors, limiting the amount of capital you can raise to fund growth. Here’s the deal. It may not be a revenue issue. It may be that you are not monitoring the right SaaS metrics. The result: you are probably spending too much. You are probably also unaware of whether this reflects growth or simply overspending, thus ruining your budget and ROI.

AWS and Azure are considered rivals in the cloud computing industry. Both offer similar services, including cloud storage, computing, container services, databases, and more. Yet, they have different strengths and weaknesses, which makes them competitors. Here’s a closer look at AWS vs. Azure to help you find the ideal match.

Metrics are crucial to maintaining efficiency, managing costs, and ensuring the health of cloud resources. Amazon CloudWatch can track over 100+ metrics by default across many AWS services such as EC2, RDS, Lambda, S3, and more. In this guide, we’ll explain the different types of CloudWatch metrics and how to set them up. We’ll also help you understand how to manage and optimize expensive CloudWatch logs through a cost intelligence approach. But first…

AWS currently offers eight main data storage services that cover different needs, from object storage to file and block storage. The most popular is Amazon S3, an object storage service. Amazon Elastic Block Store (EBS) and Amazon Elastic File System (EFS) follow. Both offer persistent, secure, and scalable storage. Yet, each has a distinct architecture and use cases. In this guide, we compare EBS vs EFS to help you decide which is better for your AWS data storage needs.

SaaS has transformed from just a convenient cloud solution into the leading driver of the rapidly expanding cloud computing sector. With its market value projected to reach $716.52 billion by 2028, nearly all organizations globally will use SaaS in some capacity. Yet, SaaS without reporting is like running a business without a budget — you’re operating but have no idea where your resources are going.

Snowflake and Databricks are both robust data platforms. Both are built for speed, scale, and cloud-native applications. They also prioritize efficiency and adherence to strict data protection protocols. Yet each adopts a distinct approach and features suited to its architecture and various use cases. It is these differences that can make all the difference in the way your data is processed, analyzed, reported, and more.