3 “As-Code” Methods that Are Changing Software Development

Our blog is dedicated to exploring the ever-evolving software development landscape, which includes examining trends, paradigms and technologies, with the goal of enhancing present practices. This particular post is part of our ongoing series within this field.

In today’s software industry, organisations have embraced the idea of “everything as code” in order to reap the rewards of streamlined operations, optimised processes and reduced maintenance tasks. They achieve these benefits by leveraging external assets and resources, which has enabled them to enhance their agility and build resilience.

Incorporating “as code” methodologies in software development can help businesses achieve improved efficiency and optimised operations throughout the software delivery pipeline. This approach involves creating configuration files in code which can automate various stages of the software development lifecycle, including infrastructure provisioning and deployment pipelines.

The “as code” paradigm has the potential to revolutionise software development projects by reducing the burden of mundane tasks on engineers, shortening product development cycles and cutting costs. To stay competitive in the industry and leverage the benefits of this approach, it is essential for development teams and software engineers to become familiar with the “as code” models that are transforming the way we work.

As the software industry continues to evolve, it is important to keep up with emerging trends and methodologies. That’s why we have scrutinised three “as code” methods that are gaining popularity and are on track to becoming standard practices in the field by 2023.

Codified Information

With the Information Age in full swing, it has become imperative to efficiently utilise the huge amounts of data available to us. While big data platforms and techniques have helped us maximise the value of data, there is a pressing need to make it more accessible, adaptable and user-friendly. This requires enabling the transfer of data across systems with ease, making it available instantly and across multiple channels.

In the DevOps-based environment of today, data sharing is often a manual process for which the knowledge and skill of experienced DevOps experts is necessary to disseminate the data to the right stakeholders and guarantee its accessibility when required. This is not an optimal scenario.

With data-as-code, data pipelines can be continuously integrated and deployed automatically, enabling data to be accessed across different cloud and workspace solutions. This is similar to what happens in software development and is beneficial in making data easily accessible. To learn more about data integration and its significance, you can refer to our in-depth blog post.

The data-as-code methodology was created to help organisations manage and distribute data efficiently, with the ultimate aim of shortening the time it takes to fulfil requests and improving communication during the iterative development process.

To ensure the timely delivery of current information, we need to treat data the same way we treat computer code, employing the same best practices in software development. This “data as code” concept is designed to achieve optimal outcomes.

To achieve this goal, we need to change our perception of data, seeing it as a constantly available resource instead of a static entity. Data in code form is expected to experience significant growth in 2023, with experts and enthusiasts already proposing guidance principles and possible avenues to explore.

Artificial Intelligence in Software

In today’s world, software plays an essential role in both our work and leisure activities. This increased dependence on software has put added pressure on development teams to deliver more advanced solutions. It is evident that software has become an integral part of our lives.

As a result of this increased pressure, programmers (along with the rest of the team) must now take into account factors beyond the code, including scalability, security and observability. To ensure these standards are met, software developers must enhance their processes while maintaining high output quality, a challenging task.

The benefits of incorporating coded intelligence into software are evident, as this technique makes it possible to build, test, release, and maintain software with an intelligent component embedded in it. With software intelligence as code, deployment across contexts is made easier through dependencies, libraries, classes, and functions connected through APIs, which allow for best practices in syntax, configuration, and design.

The goal is for teams to utilise these connections to integrate advanced software intelligence capabilities into the applications they develop. By integrating software intelligence as code resources into the development process, engineers can automate setups, take corrective action to maintain service-level objectives, and establish a framework for scalability and observability.

The goal of software intelligence as code is to simplify the creation of intelligent programs by eliminating roadblocks, standardising best practices, and delivering new capabilities quickly in any environment.

Code as Policy

By utilising Artificial Intelligence (AI) to automate the implementation of best practices, developers can codify them as a set of rules to ensure the most efficient compliance with development standards. The goal of ‘Policy as Code’ is to use a high-level code language to manage and automate policies throughout the lifespan of an application.

Human error is a common cause of problems in software development, but this approach can help minimise that risk. This is because the code itself enforces the policies, rules, and conditions related to them (rather than relying on an individual or team). Although similar to software intelligence as code, the primary focus of policy as code is on security and compliance.

Automated enforcement of rules can lead to a more efficient, transparent, and speedy software development life cycle. Additionally, policy as code promotes collaboration within a team by providing a standardised approach to policy management that can be easily understood and implemented by all team members.

Treating policy as code has the added benefit of having numerous tools that natively support it, with some even focusing on specific policy areas. For instance, there are services that allow the use of policy as code for tasks such as software versioning, deployment, and security. While there are frameworks that aim to cover all aspects, native support is still the preferred approach.

An Analysis of Everything through Code

Based on the information presented, it seems that embracing the idea of ‘Everything as Code’ and abandoning alternative models is the most practical approach. Although this is a broad concept that may encompass a range of paradigms, it is crucial to gain a deeper comprehension of it.

The approaches mentioned earlier can aid in refining the use of as-code techniques. This model allows for greater oversight of the complete software development life cycle while still enabling the use of multiple methods simultaneously without any conflicts.

Consequently, we affirm that these “as code” approaches are transforming software development by providing teams with greater oversight of their tasks, thereby accelerating the entire process. This advantage alone has far-reaching and beneficial implications for the software development industry.

For further reading on this topic, check out our Software Development Series.

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