Big data empowers enterprises to uncover valuable insights and make sound business decisions. The rigid schemas of traditional databases are increasingly unable to meet the demands of today’s data-hungry businesses. Enter data lakes — a modern approach to data management that harnesses the power of big data to unlock actionable intelligence. Here are insights you need to know right now.

Erik Peterson, founder and CTO at CloudZero, recently interviewed John-Cade “JC” Griffin to discuss how the CloudZero platform has influenced aspects of cloud financial management — such as engineering-led optimization — within his company. Some fascinating nuggets of wisdom emerged from the conversation — everything from how to answer those tricky cost questions every SaaS company struggles with to how to encourage engineers to start building with cost efficiency in mind.

As a cloud cost intelligence solution, we often get asked where we fit in the cloud cost universe — and whether or not we’re an alternative to CloudHealth. The short answer is: it depends. The landscape of cloud cost tools is busy, with lots of different solutions that take different approaches to helping companies optimize their cloud costs. To help answer this question, we thought we’d lay out the cloud cost landscape as we see it.

In today’s fast-paced cloud environment, understanding the pricing model of some of the best cloud monitoring tools like Datadog is essential in optimizing costs. With several features and adaptable pricing, clarifying Datadog costs and aligning them with your organization’s operational needs is vital for fully harnessing this robust monitoring tool. This guide simplifies Datadog pricing, offering a clear overview of its cost structure.

Serverless architectures have revolutionized how businesses deploy applications, offering scalability and cost efficiency by abstracting infrastructure management. This model, charging only for resources consumed, poses unique budgeting and cost optimization challenges due to its variable pricing structure. Navigating cloud costs in a serverless environment requires a nuanced understanding of leveraging this technology’s benefit while effectively managing expenses.

A recent Kubernetes Adoption Report showed that 68% of surveyed IT professionals adopted containers more during the pandemic. Among their goals were accelerating deployment cycles, increasing automation, reducing IT costs, and developing and testing artificial intelligence (AI) applications. But, what role does container technology play in this? This guide shares what containers are, how container orchestration works, and more.

AWS’ Relational Database Service (RDS) is a pivotal component for businesses leveraging cloud technology for their database management needs. RDS simplifies a relational database’s setup, operation, and scaling, offering cost-efficiency while automating time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. However, despite its benefits, navigating the cost structure of RDS to ensure efficient spending can be a complex task.

After watching part one and part two of our FinOps Automation Series, hopefully you’re chomping at the bit to adopt your first automation solution. In part three below, our experts — Erik Peterson (CTO and Founder of CloudZero), Alan Hand (Founder and CEO of Xosphere), Steven O’Dwyer (Senior FinOps Specialist at ProsperOps), and Mike Fuller (CTO of the FinOps Foundation) — are going to walk you through what you need to know before you take any concrete steps.

Access to critical business data, such as cloud cost data, is both incredibly important for finance, engineering, and DevOps teams, but also a privilege that should be managed carefully. This month, our engineering teams put a major focus on developing industry-leading capabilities related to access control, while also making it easier for people who require certain kinds of data to get it where it needs to be.

Many often wonder, “Is there a difference between observability and monitoring?” The thing is as IT environments have become more complex, monitoring alone has become increasingly less effective. That’s because while monitoring is crucial, it isn’t particularly suited to tracking unforeseen or unexpected turns of events. That’s what observability is meant for. This guide will clarify what observability and monitoring are – and how they differ.

If you’ve recently migrated your data from one major cloud provider to another, you may have already noticed one of our top stories from last month. If not, we’ve got good news, so keep reading! And there’s even better news if you’re the type to get excited over the chance to learn more about FinOps and hone your craft.

Research firm, IDC, published a study in early February 2024 predicting that Google Cloud Platform (GCP) customers break even after just 10 months. Moreover, the study, The Business Value of Google Cloud IaaS, reported that after migrating to GCP, the participants were on track to achieve a 318% ROI in five years. Here’s the thing. Achieving these milestones requires a robust GCP cost monitoring and optimization plan.

The first installment of our FinOps Automation Tips and Best Practices series focused on what FinOps is and why automating it can be a game-changer for your business. Now we’re back for part two, which will guide you on how to choose your optimal outcome and select the best automation options to suit your needs. Enjoy the full video for an in-depth explanation, or see the brief summary below if you’re just here for the highlights.

Whether you are a developer, an IT professional, or a tech leader in your organization, AWS Fargate promises to make provisioning, configuring, and managing your containers’ servers easier. AWS Fargate is like that cool gadget you didn’t know you needed – it’s nifty, powerful, and a bit mysterious. In this post, we’re unraveling the mystery, including what Fargate is, its key features, and how it stacks up in terms of pricing and performance for serverless computing.

Over the past decade, the cloud computing landscape has transformed remarkably, evolving from a nascent technology into a fundamental component of modern digital infrastructure. As the technology has evolved, so too has the need to develop complex cost optimization strategies. A decade ago, businesses grappled with the complexities of brand-new cloud services, often facing challenges in managing costs due to opaque pricing models and a limited understanding of cloud economics.