by • January 25, 2023
Today’s business world is relentless, and competition is intense. The digital landscape is continually shifting across many industries, creating an unprecedented demand for companies to innovate, experiment, and deliver capabilities faster. A robust digital transformation process speeds up business activities, competencies, and models while providing as much value as possible to customers. However, organizations must be cautious in their digital investment strategies. Technologies can become obsolete overnight. A successful digital transformation strategy should design a system capable of hot-swapping technologies at will and avoid vendor lock-in.
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Adopting a collaborative culture is a key aspect of modern software development and is closely tied to the success of DevOps and Agile methodologies. Collaboration refers to the process of working together and sharing information and resources to achieve a common goal. In a software development context, collaboration means working together across different teams and departments to develop, test, and deploy software.
A collaborative culture can be created by promoting open communication, encouraging teamwork, and providing opportunities for individuals to share their knowledge and skills. This can be done by creating cross-functional teams, holding regular meetings to discuss progress and challenges, and providing training and development opportunities for employees.
Implementing tools and processes that support collaboration can also help foster a collaborative culture and support a successful digital transformation. This includes tools such as version control systems and issue tracking software, as well as practices such as code reviews and pair programming. By using these tools and practices, teams can work together more effectively and efficiently, and can share knowledge and ideas more easily.
The tools and processes associated with DevOps enable software teams to deploy stable and reliable code faster and more frequently. According to DORA’s 2019 report, elite DevOps teams deploy code 208 times more often than low performers. Elite teams deploy code more frequently, have a faster lead time for changes, lower change failure rates, and quicker recovery speeds – adopting this culture will invariably force your team to implement a digital transformation in the most efficient route possible.
Cloud: The centralized nature of cloud computing environments facilitates a standardized and centralized platform for DevOps automation pipelines – resolving many of the issues inherent in centralized software deployment due to the distributed nature of enterprise systems. Additionally, most DevOps tooling is cloud-centric. Most providers have baked in tooling with batteries included – this lowers the cost associated with on-premises DevOps automation technology and provides centralized governance.
Centralized software deployment allows organizations to streamline their software development and release process, improving efficiency and reducing the risk of errors. By centralizing the deployment process, organizations can ensure that all systems are updated with the latest software versions and that all systems are configured consistently, reducing the risk of inconsistencies or conflicts.
Additionally, most DevOps tooling is cloud-centric, which means that it is designed to work seamlessly with cloud-based systems and platforms. This is important because more and more organizations are moving their systems and infrastructure to the cloud, and DevOps tooling that is cloud-centric can help them take full advantage of the scalability, reliability, and flexibility of cloud-based systems.
Infrastructure as Code: Infrastructure as code is a methodology for infrastructure automation based on software development practices. It encourages consistent, repeatable routines for provisioning and changing systems and their configuration, reducing developers’ need to manually provision and manage servers, networking, operating systems, database connections, storage, and various other infrastructure elements. It is possible to represent and manage the system in textual format (usually YAML) within a Version Control System (VCS), such as git. These files can be Ansible playbooks, Chef recipes, or Puppet manifests or used by DevOps tools like Terraform and Kubernetes to automatically provision and configure build servers, testing, staging, and production environments on the fly.
Continuous Integration and Continuous Delivery: Continuous Integration (CI) is the software development practice of regularly integrating code changes into a shared code repository. Once developers push code to the repository, an automatic build process triggers. In most cases, the build produces a docker image that is ready for deployment. Typically developers will push code to the repository several times per day, triggering several builds. Usually, a CI pipeline builds the image and then runs integration tests on the image before signing it off as a green build. Fewer bugs get shipped to production as regressions are captured early by the automated tests. Testing costs reduce drastically as the CI system can run hundreds of tests in a matter of seconds.
Continuous delivery allows a team to release green builds with the click of a button rapidly. In short, Continuous delivery provides an automated delivery pipeline in support of successful digital transformation. In practice, the pipeline automatically deploys green builds to a development environment. After an incubation period on dev, it will progress to staging, and usually, production deployments need to be manually signed off by lead devs. The release cycle complexity reduced significantly; teams don’t have to spend days preparing for releases. The automated pipeline has built-in circuit breakers and protection mechanisms, reducing cognitive load for devs resulting in a faster iteration process.
And of course, Continuous Testing is not possible without the technical path carved by Continuous integration and continuous delivery integration. Implementing automated tests as part of the software delivery pipeline to obtain feedback on the business risks associated with a software release is as rapid as possible.
Rapid digital transformation does not always coincide with robust security practices. InfoSec teams struggle to keep up with rapid code changes; there is usually not enough time for granular code reviews. Regrettably, hasty deployment can cause developers to fall into bad coding practices (insecure code, vulnerabilities without intention, misconfigured settings, passwords that are manually coded in etc.)- something made even worse by the cloud’s natural openness. DevOps teams have started using new tools that are open source and immature to monitor hundreds of security groups and thousands of server instances. Misconfiguration errors and security malpractice, such as sharing secrets (APIs, privileged credentials, SSH keys, etc.), is somewhat inevitable and can quickly propagate, causing widespread operational dysfunction or numerous exploitable security and compliance issues.
To ensure a successful digital transformation, security processes should be automated. Introducing DevSecOps – an amalgamation of development, security, and operations – can help accomplish this goal by automating the integration of secure features at every stage within the software development lifecycle; from initial design to deployment and delivery. This approach not only strengthens overall safety but also enhances prospects for success in any given project.
Automated security tools for code analysis, configuration management, patching and vulnerability management, and privileged credential/secrets management are crucial. The following is a list of a few techniques you can utilize to become secure.
Docker Image Scanning: Container images have become the standard application delivery format in cloud-native environments. The wide distribution and deployment of these container images require rigorous inspection to validate their integrity. Two robust tools are JFrog X-ray, a universal software composition analysis (SCA) tool, and Clair, an open-source tool that utilizes static analysis to discover vulnerabilities in appc and Docker containers.
Dependency Scanning: Dependency Scanning helps to find security vulnerabilities automatically (e.g., Heartbleed, ShellShock, DROWN attack )in your dependencies while developing and testing your applications. Vulnerabilities in subcomponents often occur when using outdated versions, which may lead to security problems and breaches. For example, the Equifax breach.“Using Components With Known Vulnerabilities,” is now on the Open Web Application Security Project (OWASP) top 10 list of the most critical web application security risks. Snyk, Gitlab, and OWASP have great solutions to get your started.
Secrets Management: Sometimes, the most straightforward security solutions are the most effective. As the IT ecosystem increases in complexity and the number and diversity of secrets explode, it becomes increasingly challenging to store, transmit securely, and audit secrets.
Secrets may include the following:
There are many solutions out there, and it depends on the nuances of the organization’s infrastructure setup; the following are a few recommendations.
Roy Fielding’s dissertation in 2000 proved to be the birth of the modern web APIs movement – his introduction of the Representational State Transfer (REST) architectural style for distributed hypermedia systems changed everything. An API is a set of definitions and protocols for building and integrating application software and enabling your services to communicate with other services in a black-box manner – simplifying app development. The following are several benefits of using APIs.
API management refers to the process of administering these mechanisms to ensure they are adequately developed, deployed, stored, updated, and controlled. API management also pertains to collecting and analyzing usage statistics to report upon and improving performance and is a key enabler to successful digital transformation.
API management also involves collecting and analyzing usage statistics to report on performance and identify opportunities for improvement. This data can be used to optimize the performance of APIs, identify and fix errors, and improve the user experience. Furthermore, the ability to track and monitor the usage of APIs can provide valuable insights into the effectiveness of different APIs and help identify new opportunities for innovation.
There are many API management solutions on the market. Your choice depends on the organization’s specific and nuanced set of circumstances. The following questions should help your decision-making process.
These questions will help you identify the solution that best suits your needs, both now and in the future. Of the solutions listed above, DreamFactory is perhaps the best all-rounder. With DreamFactory, you can instantly create functional, documented APIs for any data source. Simultaneously, the broad range of integrations allows you to work with almost any data source or authentication service. Try DreamFactory for free today.
Related reading: 5 Digital Transformation Trends for 2021
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