When managing data in the Azure Cloud, data extraction, transformation, and governance are the core of any organization’s data management strategy. Microsoft Fabric, combined with the Medallion Architecture design pattern, allows organizations to leverage the power of Azure to meet their goals by delivering reliable, governed data for business use and reporting.

Despite advances in technology, many businesses still struggle to effectively manage data in a cost-effective, secure, and manageable way. Cloud storage is becoming the go-to method for companies to store data, with 94% of major companies worldwide utilizing cloud computing. Corporate data also makes up 60% of data stored in the cloud. So as demands continue to grow, and compliance and data protection laws become more stringent, how can businesses effectively manage their cloud needs?

Demystify your private cloud options with our comparison of AWS, Azure, Google Cloud, and Oracle Cloud. On a typical day at Megaport, our Solutions team helps a lot of customers with a lot of questions. And in my time here, I’ve noticed these questions often follow the same theme: What’s the best way to connect to their cloud service providers? The reality is that there’s no simple answer to this question.

Snowflake is special for several reasons. To begin with, its architecture separates storage and compute, making it fast, highly scalable, and efficient. Snowflake’s cloud-native, SaaS, and serverless approach also means you don’t have to worry about provisioning servers on-premises. Instead, you just need a Snowflake subscription; their team will handle the handy work on your behalf. At CloudZero, we use Snowflake for these and several more reasons.

Over the last few years, cloud-native has become a popular buzzword in the tech industry. There are cloud-native applications, cloud-native network functions, even cloud-native storage. But, like many buzzwords, the true meaning of cloud-native has itself become clouded by overuse.

As we conclude our three-part series on key observability metrics ScienceLogic monitors, this blog focuses on the analysis and impact of user experience (UX) metrics to shed light on their business impact. Whether it’s an internal business application or a customer-facing platform, a seamless and efficient user experience can significantly impact satisfaction, productivity, and loyalty.

The IT landscape is ever evolving and with vulnerabilities only growing, maintaining the security and stability of your systems is now more critical than ever. Because any increase in vulnerabilities leads to an increase in risk. According to Verizon, exploitation of vulnerabilities increased 180% year over year. And research from Google has shown that zero-day exploits have seen a 50% year-over-year increase.

For many businesses, one of the worst scenarios is losing their data, having sensitive information leaked, experiencing hardware failure, or being attacked by ransomware. Without the necessary security measures and proper cloud infrastructure, businesses need to face the reality that they could lose millions. In today’s competitive and digital world, your business operations' safety, scalability, and efficiency must be protected.

Managing observability across hybrid and multi-cloud environments is like flying a fleet of planes, each with different routes, altitudes, and destinations. You’re not just piloting a single aircraft; you’re coordinating across multiple clouds, on-premises systems, and services while ensuring performance, availability, and cost-efficiency. AWS customers, in particular, face challenges with workloads spanning multiple regions, data centers, and cloud providers.

Microsoft Azure offers a wide range of Virtual Machines (VMs) to suit various use cases, making it a flexible platform for organizations of all sizes. However, choosing the right VM for your workload can be challenging, especially with Azure’s continuously expanding portfolio. This blog aims to provide detailed Azure VM recommendations to help optimize your cost, performance, and operational efficiency.