The increasing complexity of enterprise data architecture deployments makes it more important than ever to have an experienced professional who can act as an architect on every project. Modern data projects now require someone who can be the main person everyone else can come to for solutions and insights. The main reason why things are getting so complicated is that modern cloud deployments are making data deployments more difficult. With the cloud, data has to be more distributed, and you require more complex software to work with it. It makes having that central figure more important to our project’s success.
That central figure is crucial, but which is the right one for your specific project? Let’s break down for different types of architects that you can potentially have on your project.
The Traditional Data Architect
These traditional data architects are usually the ones who define how data can be collected, stored, and used. They essentially create the main architecture that will drive your data project. They determine the direction the business goes in when dealing with data projects. They will also be the ones who control access to that data. Data permissions is an even more significant part of the puzzle now that we have massive corporations and distributed cloud environments. They deal with the governance, as laws are now affecting the way we use and consume data today.
As you can see above, the traditional data architect is the central figure in almost everything an organization does concerning data projects.
The overwhelming complexity of machine learning architecture design has now reached a place where MLOps makes a lot of sense for most organizations. When you have such a project, a machine learning architect might be the right person to tackle the complexities your deployment has to deal with. Since machine learning projects are cyclical, the architect has to be flexible enough to ensure that the right strategy is being chosen at every phase. They also have to be able to communicate with the various teams within the machine learning project. That is because most machine learning projects are done by data scientists and machine learning engineers along with the various stakeholders and executives in the company. They also have to Institute a data engineering architecture that will scale to wherever the company needs it to go.
The enterprise architect is responsible for laying down a great foundation to manage information inside what is possible for a corporation’s data needs. These architects are usually working hard to ensure that the corporation is compliant with various privacy laws and regulations.
They are also the ones that set the tone that the workers will have to follow. For example, they will have clear policies on how various people in the organization can use and access data. The enterprise architect is responsible for choosing the best architecture possible for the project. They are also there to ensure that the overall project steers clear of anything that could hinder progress.
The Architect Specializing with the Cloud
Cloud computing has made infrastructure a specialized domain within the data science world. You now need an architect that specializes in managing various aspects of whatever cloud you are working on. For example, Amazon has its own specific services and virtual machine instances that you can work with. You need an architect who understands the back-end architecture of whatever cloud platform the organization has decided to use.
The right data management architecture can be the difference between a smooth sailing project or one that stalls completely. These architects are also responsible for monitoring changes in cloud services and ensuring that these services stay up over 99% of the time. While this position isn’t purely about data engineering, it has a massive effect on whether your project will be completed successfully or not.