DevOps + Data: DevOps for Data Management

And what if we apply the existing DevOps techniques and patterns to the management?

DataOps is a new algorithm for interaction between managers and development teams. The methodology implies that the main emphasis is on establishing effective interaction between departments of the company, high-quality data exchange, and productive communication and cooperation.

As a result of this approach to managing and maintaining IT projects, it is possible to combine disparate practices, tools, and approaches, which enriches the final product that the team is working on. This is due to the fact that such interaction brings the work on an application or web service to a new speed. Development is faster and with fewer edits because IT is always up-to-date with changes and current requirements, and analysts and project managers understand at what stage the development of every module is.

This is the way our team works. Developers and different departments maintain close relationships, resulting in a smooth deployment of software solutions and updates to them. We have tested different approaches and found that this process management is the most reliable, safe, and profitable both for the company and for customers and consumers.

Fresh Approaches to Effective Data Management

Data are a valuable resource, and timely access to them, analysis, processing, and correct reaction to events and changes in the project are the key factors for the success of a software product in the market. Problems with data management are the main stumbling block on the road to success.

The DevOps culture forces us to rethink outdated paradigms and introduce new ways of managing data.

To ensure that your IT departments work efficiently and smoothly, always have up-to-date information, and achieve higher productivity through data sharing, it is worth focusing on three key things. Implement these ideas into your processes, and you will see results very soon.

Playable data

Cloud computing engineers are accustomed to deploying environments at the click of a button, but data collection and storage solutions are not so straightforward. Ideally, they should be just as simple and not raise questions about how to access the information you need. Therefore, the collection and reproduction of data should be the same smooth and simple processes. With this approach to collaborating on data, questions about the impossibility of obtaining the desired data or problems with obtaining re-access to them will disappear.

Analytics as code

YAML files help cloud engineers to describe in detail every asset in the cloud and understand how all parts work together. However, in the analytics process, it has become more common to rely on professional dashboards for detailed information. “Analytics like code” thinking can work similarly. In this case, actions on data can be considered code and configuration, thereby conveying information to the user using a reproducible data pipeline. In this case, IT departments can automatically manage individual nodes in the cloud environment and be more flexible in the process of making changes or restoring the software infrastructure.

Data as a platform

The third step after a reproducible data pipeline and operational analytics is to create effective decisions based on the information obtained. These solutions must be easily scalable, offering data as a platform – this is the main task of a cloud engineer. This methodology enables developers to run their environments for rapid prototyping and create valuable software solutions.

So, DataOps is becoming the paradigm best suited for cloud products, fueled by the rise of AI and machine learning. The work of a cloud engineer to coordinate the workgroup and optimize data becomes integral to this approach; so, a DataOps-centric viewpoint is the most viable.