4 Keys to Effective Automation
Tech leaders are discovering just about every process in the software life cycle can be improved with automation. Ruthlessly effective automation takes this belief to another level. Automation is becoming a key differentiator in the pursuit of competitive advantage.
Cloud services, platforms, and tools are ushering in the need to automate every process in your software development practices. End-to-end automation is modernizing design, testing, quality assurance, security, performance testing, resource provisioning, release integration, continuous delivery, monitoring, feedback, and more. The only limit to automation is a lack of imagination and the ability to harness the tools and frameworks to assist your efforts
Automation doesn’t have to be perfect to be good. The objective is to deliver measurable results and initiate continuous improvement. An automated process helps to break down barriers within organizations and eliminate silos. Automation reduces the risk of failure during handoffs which are the weakest part of any process.
If you're ready to build a training program and skill up your DevOps team, connect with a Tech & Dev Specialist now!
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Market researchers, academics, industry analysts, technology consultants, and other visionaries all agree the best way to advance automation is with DevOps.
DevOps is undeniably a crucial component for companies looking to move to a model of constant delivery of services, applications, and systems. DevOps practices can help most organizations eliminate silos while implementing continuous process automation.
When DevOps is done well, it unifies people, processes, and technology to deliver value. DevOps isn’t a new or radical concept. DevOps' heritage traces back to lean manufacturing and Agile development, which celebrate the virtues of small, fast, and simple. DevOps expands on these building blocks. Customer feedback loops inform the iterative development process guided by DevOps.
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Data-driven decisions are only as good as the quality of the underlying data. The same is true for insights gathered from data analytics. Using the Python programming language, you can take responsibility for data quality, analytics, and visualization with one powerful tool. Python makes it feasible to build trusted data pipelines that can be deployed in real-time to power your AI applications, services, or products. Python is the fastest-growing (by nearly every measure you look at) and the most highly rated language for collecting, cleaning, modeling, and visualizing data. Python programming is democratizing data.
DevOps and Python are the dynamic duo for Ruthlessly Effective Automation, a Webinar to help Tech & Dev leaders unpack the essential principles of accelerating software delivery through process automation. This session explores factors critical to digital transformation's success, the backbone of an organization with a DevOps culture.
Process is a critical element that needs to be planned for automation. Continuous integration and continuous delivery allow for better and more efficient development of products and services that meet your customers’ demands. For CI/CD Automation, many factors can enable or hinder the successful deployment of applications, services, and products.
Testing automation for microservices will help ensure that all your various component-level services are working as required and as efficiently as needed. Having a well-thought-out Microservices architecture in production will bring many benefits to your development team. Flexibility. Scalability. Maintainability. Testability. And can be tuned and tested more easily for performance and customer functionality.
One of the key advantages of microservices is flexibility.
It's relatively easy to troubleshoot microservices, and that’s because these are independent processes that can simply be removed from the application and replaced while the application continues to run. So you can get to a point where there is practically no downtime with a microservices architecture.
If you architect your microservices well, they will be a bit more failure resilient. If one service goes down, then other parts of the architecture can continue to operate.
When you contrast microservices with Monolithic systems, applications, and services, you can quickly see how the monolithic systems are brittle, more fragile, and tend to fail altogether, rather than one component. Successful businesses understand the need to deploy microservices throughout their organization. This brings on a new challenge of complexity because of the need to orchestrate all the moving parts. There are some great tools to help in orchestration, like Kubernetes. The benefits of moving to microservices and seeing an increase in orchestration complexity outweigh staying with a monolithic system that is brittle, bloated, and difficult to change to meet market demands.
DevOps. Python. CI/CD. Microservices.
Dive deeper into how all of these elements come together to make your team thrive with Ruthlessly Effective Automation. Sign your team up for access to an upcoming Bootcamp with reknown expert Noah Gift to learn how to unleash your team’s edge with DevOps and Python.
Bootcamp leader Noah Gift, is a lecturer at the Duke Graduate Data Science program, Northwestern Graduate Data Science program and UC Davis Graduate School of Management in the MSBA program. Professionally, Noah has approximately 20 years’ experience programming in Python and is a Python Software Foundation Fellow. He has worked for a variety of companies in roles ranging from CTO, general manager, consulting CTO, and cloud architect. Currently, he is consulting start-ups and other companies on machine learning and cloud architecture and is doing CTO-level consulting as the founder of Pragmatic AI Labs.