Navigate the nuances of optimizing Canadian government training budgets and spending to ensure employees are skilled for their job-required technology.
Companies spend hundreds of thousands of dollars on new and existing technologies yearly but often don’t get the best use out of them. Technologies typically aren’t plug-and-play. It takes talented IT professionals with the proper knowledge and skills to use them efficiently to solve problems each time they must tackle a new task.
It might seem as if IT professionals who have been working with a specific technology for months or years know what they’re doing. But about 40% of students who have worked with a particular technology for some time and then took a course in it with ExitCertified tell our instructors that they learned new skills that could have saved them hours. Most IT professionals working with a specific technology don’t know what they don’t know. But they are ecstatic when they learn something new.
In this blog post, we’ll look at some of the top methodologies and technologies you could optimize if your IT team were using them better.
These days, many technologies are related to the cloud. According to IT Spending and Staffing Benchmarks, a 2022 study from Computer Economics, the cloud is the top spending priority. A net 83% of survey respondents are increasing their spending on cloud applications, and 73% are increasing their spending on cloud infrastructure. Data analytics is the third most popular area of cloud spending (65%).
To get the most out of your cloud investment, you should upskill your team with authorized training from your cloud vendor, such as AWS, Microsoft Azure, Google Cloud, or OCI. But no matter whether you’re in the cloud or not, there’s one thing that every organization can do to help them work efficiently: embrace DevOps.
With cloud applications being the #1 area where organizations spend money, it’s imperative to look at how well your business practices DevOps. DevOps combines cultural philosophies, IT practices, and tools to more effectively and efficiently deliver applications and services at high velocity.
DevOps brings development and operations together so they work together rather than in silos. When these teams merge, they work together using tools to automate processes to develop, test and deploy reliable applications. Suppose you’re not doing a good job of practicing DevOps. In that case, you probably have a lot of manual processes that are unnecessarily taking loads of time and delaying the deployment of new applications and additions to existing ones.
Many companies begin by combining their operations and development teams — and stop there. This approach encourages collaboration but does not address the processes and methodologies that slow down software development. To effectively implement DevOps, you should examine all software development and operations pipeline components so you can adopt a set of practices, processes, and philosophies that remove barriers to efficiency.
Medium to large-sized teams should use project management frameworks like Scrum and Kanban to manage workflows most efficiently. Both frameworks reduce the time it takes to develop applications and help you see daily what is working, which areas of a project are moving ahead, and which need to catch up. While you can get a good understanding of both of these frameworks online, a class will teach you many more ways to use them.
Infrastructure as Code
One of the tools used in DevOps is Infrastructure as Code (IaC), which helps you manage your development, testing, and production environments in a repeatable and efficient manner. Many companies have not put IaC in place because it requires a significant time investment to learn and set up IaC tools like Ansible and Terraform. But that’s no reason not to implement it. If you have not implemented IaC, you’re wasting lots of time daily. Just as it might initially take you an hour to set up a group in your email contacts of 50 people, once you set that up, sending out a bulk email is far more efficient than emailing everyone individually. Setting up IaC will save you far more time than it took to set it up. Once you’ve created the initial IaC process, you can use it across the enterprise to categorize devices into groups so you can automate them all at once. IaC forever thereafter generates repeatable and identical infrastructure for any environment or group of environments, providing you with the same infrastructure every time it deploys, preventing configuration errors.
Continuous Integration Continuous Delivery
Continuous Integration Continuous Delivery (CI/CD), or the CI/CD pipeline, consists of steps that automate the software development lifecycle. In version control systems, a “commit,” best explained by Wikipedia, is an operation that sends the latest source code changes to the repository, making these changes part of the head revision of the repository. Unlike commits in data management, commits in version control systems are kept in the repository indefinitely. Using automation tools like Git and Jenkins, developers can commit code in small increments, sometimes multiple times a day. The code is then automatically tested before it is merged in a shared repository. The tools will automatically test the code, and the developers will find out whether or not that code passed some basic tests. If it didn’t pass, they’ll know what portions of the code did not work and which previous version of the code they’ll need to revert to, to fix the code.
Without this automation, developers might check code into a repository after conducting initial tests on the code, and then someone else would need to do more extensive testing. Meanwhile, other developers would be building on top of that code already in the repository, even though it has not yet been fully tested. So, if that code had errors, other developers who were building on top of that version might also have to make changes to their code. This manual system results in a lot of time lost for the development team.
Developers today are often embracing microservices architectures to develop applications. Microservices is a type of architecture that separates an extensive application into smaller independent parts, with each part having its own realm of responsibility. Microservices differ from the traditional monolithic application architecture, which is used to create one application all in one piece.
While developers still use a monolithic architecture for applications that are built with only a handful of functions, microservices architecture is best for bigger applications with multiple functionalities. One reason for that is if one function in a monolithic application breaks, that failure can cause a ripple effect and cause other things in the application to break because the functions are tightly coupled. To repair even just one function in a monolithic app, a developer must take the entire application offline.
With a microservices application, because each function works independently of other functions, it should have little effect on the other functions if one function breaks. One of the best benefits of a microservices architecture is that you can deploy new functions continually without interfering with the application. Whereas if you want to add a new function to a monolithic application, because it’s all built in one piece, you must take the entire application offline—which prohibits its use—and then you need to be sure that the new function doesn’t interfere with other functions in the application. When redeploying just one microservice, you just take that one service down temporarily to redeploy while the rest of the application remains working online. Because functions in microservices aren’t tightly coupled as they are in a monolithic app, the functions aren’t interdependent on one another. This also means that different development teams can work on different parts of the application in parallel, without stepping on each other’s toes.
There are different ways to go about creating microservices. You can use containers or serverless. You’ll learn about which one would serve you best when you take a training course in microservices and orchestration tools like Kubernetes.
Before public clouds became popular, organizations created their own private clouds. But companies still use private clouds to maintain complete control of their data. However, even though they may have a private cloud, organizations realize they can still benefit by running some workloads like applications and data analytics in a public cloud.
Applications in a Hybrid Cloud
A hybrid cloud ― a combination of a private cloud and a public cloud ― gives you unlimited elasticity and scalability that you might not have with just a private cloud. And it allows your applications to stay online when your private cloud fails. You’ll need to know precisely how to set up your infrastructure to accommodate sudden bursts of demand on your private cloud. You’ll also need a good understanding of both private and public cloud infrastructures, as well as various tools to help interoperate across boundaries and architectures to provision your infrastructure and manage both environments from one pane of glass. Of course, there are costs to moving data to a public cloud, and that’s why it’s essential to work with a trained instructor who knows where the pitfalls lie so you can keep those public cloud costs under control.
Traditional on-premises analytics gives organizations total control of their data but misses out on some of the advantages of moving analytics to the cloud. For example, processing big data in on-premises data centers can take organizations hours to run workloads because they don’t have the seemingly infinite machines that cloud providers have. With cloud analytics, you don’t need to overinvest in devices that are only needed temporarily. In the cloud, you can run analytics in a fraction of the time it takes on-premises because the cloud provides unlimited machines to perform your computations. The best part is that you only pay for the machines when they are in use.
The public cloud is perfect for holding a data lake, which holds any type of data (structured and unstructured), offers inexpensive and unlimited storage capacity, and offloads management to the public cloud provider. While you could handle all of these things in a private cloud, it takes a lot of effort and capacity planning, and you probably don’t want to spend your time manually managing all the aspects of your data lake. But before handing over your data lake to a cloud provider, you still need to know how to secure, use and classify the data so that your data lake does not become a data swamp. Every cloud provider is different, so you’ll need a thorough understanding of how your cloud provider handles your data lake and the different options you have for using other data technologies like Databricks and Snowflake to work and collaborate seamlessly across multiple clouds no matter where data, applications, or your local and global business communities reside.
The secret to optimizing your technologies lies in implementing the best methodologies and frameworks like DevOps, Scrum, and Kanban so that your IT teams work together to complete work most efficiently. Teams also need professional training in the technologies they use. While there are many ways to do things, some are efficient, and some are inefficient. When it comes to the time your IT team spends working on projects, it makes a lot more sense to do things efficiently and effectively.
To speak with an IT engineer who can help you or your IT team’s current situation and needs, contact us.
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