3 “As-code” Approaches Reshaping Software Development

The “As code” model is booming across the software industry. With plenty of benefits and a lot of potential, here are 3 examples that illustrate why that’s happening.
July 19, 2022
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This article is part of our Software Development Series, in which we take a look at the current state of the development industry. In it, we aim to discuss new paradigms, trends, and tools, as well as share recommendations and suggestions to improve software development operations and approaches to better fit current and future demands.

For some years now, the “everything as code” model has been establishing itself as standard practice among software development companies. That’s because these businesses are now seeing that using these external assets and resources to automate tasks, streamline processes, and avoid maintenance activities is the best way to increase their flexibility and achieve resilience. 

With their adoption of “as code” approaches, software companies leverage a practice that enables them to use code-based configuration files to make certain tasks more efficient and improve processes across the software delivery pipeline. The “as code” approach seeks to automate different tasks within the software development lifecycle, from infrastructure provisioning (probably the most widely known “as code” solution) to deployment pipelines. 

The “As code” model can really streamline a software development project, relieving developers from rote or boring tasks, reducing overall time to market, and decreasing costs, among other benefits. That’s why it’s important for development teams and software engineers to know the “as code” models reshaping the sector since they are changing how these teams and individuals work.

That’s why we wanted to review 3 of the “as code” approaches that are getting more and more attention in 2022 and which are on the path to becoming industry standards. 

Data as code

As we move deeper and deeper into the age of data, a lot of people are starting to wonder how we can better use the insane amount of information available to us. Big data platforms and methodologies have helped us clean, refine, and sift through vast data sets, but we still need it to be more manageable, more flexible, more convenient to use. In other words, we need data capable of moving around in different environments, ensuring immediate availability and shareability.

In the current DevOps-centric landscape, sharing data between different teams, colleagues, and systems is often a manual activity that requires DevOps experts to grant access and ensure availability of data to everyone that might need it. That’s far from being ideal. 

That’s why data as code is such a good approach: because it allows companies to automate the continuous integration and deployment of data pipelines, making data available through different clouds and workspaces. It’s like what happens with code when developers are working on it.

That’s precisely what the data as code approach is here to do. Through it, teams can process, manage, use, and share data easily. The ultimate goal is to accelerate the development of iterations while increasing collaboration, tearing down the request-and-wait flow of current processes.  

So, data as code pushes us to treat data in the same way we treat code. This means using the same best practices we use in software development but applied to data and all its related processes: versioning, automated testing, CI, and more, with the ultimate goal of delivering relevant and updated data fast. 

Doing so requires a mindset shift that stops treating data as if it were an immutable asset and starts seeing it as a dynamic resource that needs to be everywhere, at all times. Some experts and enthusiasts are already proposing principles and paths to explore when moving forward but it definitely feels like data as code is about to take off during 2022.

Software Intelligence As Code

The importance of software in today’s world is impossible to understate. Basically, digital tools are essential for most of our daily activities, from work to entertainment and beyond. That’s why companies developing them are pushing development teams to provide better and more sophisticated solutions: the race to offer the best tech apps is on.

That pressure leads developers (and the entire team, to be honest) to start considering aspects that go beyond the code itself. Scalability, security, and observability are but a few of those aspects. To guarantee that, software engineers need to improve their development processes, reducing complexities but without harming the quality of the end products. It’s a tricky task.

That’s when software intelligence as code comes in. Behind that name hides a process through which the best practices for developing, testing, delivering, and managing software intelligence get coded into the software code itself. Using dependencies, libraries, classes, and functions through API connections, software intelligence as code can easily extend the best practices for syntax, configuration, and architecture, guaranteeing easy deployment across environments. 

The idea is for teams to use those connections to bring sophisticated and well-developed software intelligence capabilities into the applications the developers are working on. Integrating software intelligence as code resources into the development process, engineers can automate configurations, implement corrective actions to ensure service level objectives, and lay down the foundations for scalability and observability. 

Ultimately, what software intelligence as code is after is providing an easy way to develop applications in a smart way. It does so by removing obstacles, streamlining the use of best practices, and providing additional capabilities across environments and in no time. 

Policy as Code

If software intelligence allows for developers to automate the implementation of best practices, then policy as code is the best way to automatically enforce development policies. The idea behind policy as code is to provide code written in a high-level language to control, manage, and automate policies across the entire application development cycle.

Using this approach, development teams can reduce the number of potential mistakes due to human errors. That’s because the code itself (rather than an individual or team) is the one enforcing the policies and its related rules and conditions. While that might sound a lot like what software intelligence as code can do, the reality is that policy as code is mostly about compliance and security while software intelligence as code extends beyond that. 

Automating the enforcement of policies is key to achieve a more efficient, visible, and faster software development life cycle. That’s not all: policy as code increases the collaboration capabilities of a team, as it provides a uniform means to manage policies that anyone can quickly understand and implement. 

Another thing about policy as code is that there are several tools that natively support it, even to the point of specializing in specific policy fields. For instance, there are services to apply policy as code for security, deployment, and even software versioning. And while there are certain frameworks that are trying to encompass it all, the reality is that they haven’t beaten the abilities of native support.

Everything as code, Deconstructed

While reading here, you might get the impression that the safest bet is to use an everything as code approach and avoid using separate models. It’s reasonable. However, everything as code is more of a general approach that, while it might serve to encompass some of these models, still needs further definitions. 

Using the approaches above can get you closer to more precise definitions on how you use as-code approaches. They help you manage the as-code model with more granularity, giving you more control over your entire software development life cycle but offering you the possibility of working with different approaches in tune and with no integration issues.

That’s why we’re saying that these “as code” approaches are reshaping software development: because they give teams the ability to manage their operations better, streamlining the whole workflow. That’s a major advantage right there, something that will definitely change the development sector for the better.

More related articles in our Software Development Series.

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