Application and infrastructure development has always been a difficult task. Though the development teams aim to build feature-rich experiences with seamless integrations, yet with increasing complexity, there is always an increased risk of insecure, slow, and unstable software.
To overcome this, development teams need faster ways of testing pre-release software and collaborative workflow management to orchestrate the development process. They need an agile software development environment that minimizes administrative overhead and expedites the meantime to deployment (MTTD) for new releases.
Perhaps the most important aspect of DevOps is the philosophy behind its use. Development teams are experiencing increased demand for rapid deployment but can’t risk releasing software that is incomplete or error-ridden. Doing so would negatively impact the end-user experience and create additional workloads across the business, as customers report problems with websites or applications.
To remediate this challenge, DevOps aims to create agile and scalable systems that foster a culture of collaborative software development. These purpose-built systems assist development teams - from the initial brainstorming phase through to actual deployment on the network. With recent security concerns, continuous security is now part of the DevOps evolution. An additional skill set is slowly making its way into the DevOps arena, machine learning. Machine learning is slowly becoming a welcome solution to the growing continuous speed of deployment. As more and more methods of solutions make their way into public git repositories, the algorithm works to help create better predictions, establish analytics, create AIOPs catalogs, and more. Integrating machine learning into your DevOps solutions with major objectives
With that philosophy in mind, you now need to put those ideas into practice. The primary way of achieving more agility and scalability during development is through new hardware and software infrastructures and new development methodologies for your dev team.
CI/CD – CI/CD or Continuous Integration and Delivery is a way of promoting collaboration between individual developers during the development phase. It is an agile development methodology that allows development teams to meet business requirements, maintain high code quality, and promote security through deployment automation.
When a developer finishes a new project, the code is merged into the main branch and tested for validity. If the CI/CD tool detects any syntax errors or undeclared variables during compile-time, your developer will get an alert with the details. These compile-time errors are typically borne from flawed developer code and are entirely preventable through CI/CD analysis.
Managing runtime errors can be much more challenging for development teams as they can find their way into a public release without being noticed during the debugging phase. Such runtime errors are seldom caused by bad code and mostly relate to the operating system or architecture on which you are attempting to run the code. This makes it difficult to predict them without comprehensive pre-release testing of your software. A CI/CD tool can help by performing an in-depth runtime analysis of your project, testing all functionalities across multiple OS and architectures, to ensure no bugs are shipped with your final release.
Implementing Microservices – It may seem counterintuitive to split up parts of your application; however, this can have a positive impact when managing your development cycle.
An example of microservice usage can be found in Google’s Android operating system. For years, essential parts of the operating system were locked down and received updates once or twice a year with a major new OS release. With Project Treble and Project Mainline, they are modularizing critical aspects of the OS to expedite the delivery of security and performance updates. Currently, this includes security definitions, application runtime frameworks, and hardware drivers so that they can deliver continuous updates despite slow development cycles from third-party hardware manufacturers.
The same philosophy can apply to your application and website development. By splitting core functions into smaller modules or microservices, you can reduce the risk associated with deploying new code on your network. Instead of killing your entire service to revert to a previous version, you kill a specific module, minimizing service disruption. This also complies with the agile DevOps methodology, which distributes development effort across multiple smaller projects.
Track Delivery through Machine Learning
Anomaly detection is an excellent machine learning tool that can be easily added or integrated with CICD. An example of where anomalies can take place is when activities that process a large amount of data created by developers that inadvertently or accidentally create wrong triggers -hence anomalies. The assumption is that DevOps accidentally create these mistakes that can be be easily fixed, but what if these artifacts are malicious? Anomaly toolsets with required approval process make any Release managers gatekeeper. The anomaly toolset can also work to detect malicious code as well.
Increase Quality of Service through reinforce algorithm
Speed alone is not enough to generate a successful predictive quality service. Customers today are quick to judge and even quicker to let someone know a good or bad experience. With the use of machine learning, you can narrow down that negative experience turn it to a positive outcome. With learning and reinforce algorithm, we can ensure that customer quality service goes towards a positive outcome.
Trianz is a leading DevOps consulting firm with vast software development and IT operations management expertise. We understand the potential of DevOps in simplifying the development lifecycle. That’s why we work with you to minimize obstacles for your development teams so that they can deliver industry-leading digital experiences to your customers.
Get in touch with our DevOps consulting team and start applying best-practice DevOps methodologies today.
Contact Us Today
For decades, Windows served as the workhorse of the business world. In recent years, however, a significant transformation has occurred with the rise of cloud infrastructure platforms. Enterprises now realize that legacy on-premises Windows workloads are impeding their progress. Core challenges include licensing costs, scalability issues, and reluctance to embrace digital transformation.Explore
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore