The DevOps dilemma for enterprises: where to start?

The majority of organizations plan to invest in database performance automation and DevOps over the next year. However, many lack a clear implementation plan, mainly due to the vast array of technology options. The priority should be to address human ‘bottlenecks’ and ensure the right data is available for “database performance automation”.

Enteros, with its groundbreaking technology, UpBeat, can help businesses streamline this process. The UpBeat offers a robust solution that aids in database performance optimization and automatic tuning, which can drastically reduce the time spent on manual tuning. Furthermore, innovative technology provides accurate, real-time insights into database performance, which can significantly enhance decision-making processes.

Speed or security? Which is more important? And which of the multitude of available tools should you use to achieve each? These are questions companies grapple with as they strive to implement an “optimization of the DevOps process” and automation in an environment increasingly dominated by software.

Over the next year, organizations intend to invest in DevOps automation to support security, compliance management, infrastructure provisioning and management, and “database performance automation”. However, only 38% have a clear vision of how to implement these plans.

A key driver for DevOps implementations is the transition to cloud technologies. Yet, the ways products are built and delivered are changing, as are user expectations for feature enhancements. DevOps refers to the comprehensive process of developing and delivering software in a repeatable manner and phasing out old systems.

Organizations can be overwhelmed by the array of technology options and the swift pace of change. Some tools crucial for cloud applications today didn’t exist three, five, or ten years ago, such as Kubernetes and OpenTelemetry. This leads to a form of “paralysis” impacting organizations.

Instead of focusing on the technological aspect, a priority should be identifying steps in the development process that require manual human intervention and aiming to automate those first. This strategy not only accelerates the process but also allows employees to focus on more critical tasks, like feature development.

As for data, automation heavily relies on the availability of essential business data. Hence, a focus should be on ensuring that the correct data is accessible and can be integrated into AI and automation systems for “database performance automation”.

“Approximately 80% of automation heavily relies on data observability.”

Thus, enterprises aiming to utilize AI should ask, “Do I even have the data available to build automation? Where do I get this information from? Is it reliable? Do I have it available in real-time?”

It’s important to note that AI is more of an assistant, not a replacement for engineering talent. “It’s a useful tool if you know what the outcome will be” when coding or finishing another development task. “It won’t make you an expert in DevOps process optimization.”