Operations | Monitoring | ITSM | DevOps | Cloud

I find it amazing how much opportunity and flexibility cloud environments are creating for organizations of all sizes. I’m seeing more and more companies experimenting with open-source software (OSS) relational database systems, which years ago would’ve been too complicated for the customer to set up. With Azure, you can spin up OSS systems like MySQL or PostgreSQL quickly to determine if the engine fits their needs. If it does, you can continue development on it.

If you have decided to move your infrastructure and workloads to the cloud, you know that there is no one-click shortcut for carrying out the migration process. There are multiple factors and particularities of your infrastructure to consider when planning and subsequently taking the necessary steps for cloud adoption.

If you have arrived at this page but need a quick refresher on what Azure Active Directory is and why it is different to Active Directory that you might have installed on-premises, you might like to first read this quick run-down – What is Azure Active Directory.

To optimize its cloud investments, your organization needs internal stakeholders to act on shared knowledge about its cloud costs and cloud usage. But in practice, it’s difficult for organizations to gain a high degree of clarity about their cloud spending. The factors contributing to cost data are not normally visible to all stakeholders, and it’s often impossible to attribute costs to the teams, services, and applications that incurred them.

Microsoft's Azure and Amazon Web Services (AWS) are the two most popular cloud providers today. They both offer a variety of Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) solutions. In addition, you'll find products covering multiple computing areas, such as compute, storage, analytics, and networking. You can also deploy AWS or Azure services in the cloud, on-premises, or as a hybrid setup.

Kubernetes has broken down barriers as the cornerstone of cloud-native application infrastructure in recent years. In addition, cloud vendors offer flexibility, speedy operations, high availability, SLAs (service-level agreement) that guarantee your service availability, and a large catalog of embedded services. But as organizations mature in their Kubernetes journey, monitoring and optimizing costs is the next stage in their cloud-native transformation.

As a developer, I love the versatility of Python. Over the years I have used Python for so many different use cases: game development, APIs, IoT, machine learning, and web development. It can scale tall applications in a single bound and take on any challenge faster than you can pip install flask. Something you learn very quickly in the world of app development is to build everything for scale.

Cloud Service Providers (CSPs) offer an ever-expanding array of instance types, ensuring that for any given workload there exists the perfect hosting option that matches the exact needs of that app or business service. But with this expansion comes an ever-increasing challenge to match the workloads to the offerings – there are many things to consider.