Cloud technology has already helped define the ‘new normal’ and shape the digital landscape. It’s a way to improve how people communicate, operate and develop within the competitive business environment. By introducing grid computing into the cloud, you are effectively escalating, scaling and generally improving your own businesses operations. With grid computing you get speedy research performance. In other words, farming out large problems to be solved using parallel processing across many worker nodes or running multiple different jobs at once, speeds up research throughput. Also, the elastic capacity of the cloud allows for the freedom to adjust your cloud grid computing resources based on immediate needs. This denotes that you can scale up or down as necessary, of course. In addition you unlock a wide range of powerful cloud tools.
Things also become cost-efficient very fast, as cloud solutions eliminate the upfront cost of IT – unlike traditional research computing that may require upfront decisions on the technology and the purchasing of all the required machines before they can be used. Rather than being tied down to one location, your data and applications are available and accessible wherever you are and easily shared across multiple teams and offices.
Where do we go from here? Well, for the past few years it has become obvious that there are other methodologies that can enhance the process of running complex workloads. Staying serverless is the focus, but beyond that, we should look at a variety of valuable cloud-powered tools and Kubernetes (or more widely known as k8s). Kubernetes is an open-source system that automates your application deployment, scaling, and management.
Incorporating Cloud Tools: Azure Batch and Azure App Services
The most important facet of serverless compute platforms is giving developers a chance to concentrate on application and script development, versus spending time and energy managing its underlying infrastructure. The goal is to handle huge workloads via a platform that was crafted as an elegant serverless solution; the sort of solution we facilitate regularly here at Hentsu. So, when moving to the cloud environment, the customary method is rewriting the code, thus creating a modernized cloud native stack. For example, batch work can utilize Azure Batch; scripts are turned into Docker containers, which are scheduled onto temporary machines. The key term there is ‘temporary’ – the machines are used and paid for only as much as necessary, with provisioning handled completely automatically.
The main drive here is easy management, lower cost, and parallelism. A parallel for application servers is hosting them with Azure App Service, which denotes reliability and even more useful management tools.
However, if you are looking to utilize powerful systems such as Kubernetes instead, you must understand certain basics first. Kubernetes is currently one of the most popular methods of running production workloads at scale, but more than that it brings other big cloud-based advantages, as well as vital improvements for enterprises. Kubernetes is great for building containerized and resilient applications.
Kubernetes in the Cloud: 4 Essential Benefits for Enterprises
When it comes to data processing, the huge Kubernetes ecosystem has truly turned things around, offering a variety of ways to reduce its generally high complexity. The immense advantages of Kubernetes may get you to where you want to go, however managing it is a great challenge, especially if you are doing things on-premise, where you may well need someone to manage the infrastructure full-time. In contrast, AWS and Azure can do that bit for you; a reliable, managed cluster is only a few clicks away.
Let’s have a peek at the biggest advantages of Kubernetes to any business or enterprise:
When you’re developing applications and running workloads, Kubernetes delegates infrastructure utilization and boasts the ability to scale up in case CPU usage threshold is surpassed. As soon as the workload is reduced, Kubernetes scales back the application.
Flexible and Cloud-friendly
The massive benefit is being able to utilize several existing tools for cloud-native software. Staying true to the general scalability and flexible nature of the public cloud, Kubernetes is a great addition when resolving issues with clusters and deployments.
Apps You Run Are Stable
With Kubernetes applications are up and running quite quickly and they are much more stable. In addition, updates and changes can roll out without any downtime.
Smooth Cloud Migration
Since K8s runs consistently across all environments, on-premise and clouds like AWS, Azure and GCP, Kubernetes provides a more seamless and prescriptive path to port your application from on-premise to cloud environments.
Utilizing Databricks and Other Azure Collaboration Cloud Tools
Azure Databricks is another different and rather vital product for data experts, focused on allowing them to analyze data efficiently and smoothly. One of the biggest additional advantages is having actionable insights, which are understandable to non-data individuals within your company. For instance, if the data team delivers new data, business execs, marketers or sales can go over the data without requiring too much technical knowhow.
Benefits for data engineers: a clear major advantage for data engineers is the ability to create, clone and edit clusters of intricate data, which they can transform and deliver effectively for review to data scientists and data analysts.
Benefits for data scientists: they run advanced analysis on the same cluster of data in one interface – Databricks auto-scales within the cloud, thus decreasing the resources needed for optimized performance.
Azure Databricks is easily integrated with numerous data analysis and storage tools from the Azure library, such as Azure Cosmos DB, Azure Data Lake Storage (ADLS), Azure Blob Storage, Azure SQL Data Warehouse, or reporting via Power BI which is known for its user-friendly dashboards. Thanks to the elasticity of the cloud no matter how complex the data is, all the gathered information becomes much more accessible for non-data experts.
Adopt Your Own Cloud Sanctuary
Cloud grid computing is the future for research, and it helps companies shift from traditional workstations to the grid, thus leading to lower operational costs, auto-scaling, and speedy research performance. We have already taken a closer look at why grid computing is important for today’s large financial companies (hedge funds, and financial services). To learn even more, feel free to check out our recent post: Infinitely Scalable Clusters: Grid Computing 101.
The bottom line is, if you have open-source tools such as Kubernetes by your side, they will no doubt help reorganize your business towards better production. However, the ultimate goal is quality, saving time through automation, and managing things more efficiently. Utilizing reliable tools that are cloud-based, means you have all the heavy lifting done for you.
That’s where Hentsu and public cloud services’ features and functionalities come into play, complete with a solid infrastructure foundation.