If you’re a DevOps engineer, you probably don’t think of data operations as a key part of your job. You help to write code and deploy and manage applications, while someone else handles data management and analytics, right?
Probably not. Although it might be comforting to imagine that every company has a dedicated team of data specialists on staff who oversee data operations, the reality is that the vast majority of companies employ no data scientists at all. Only around 6% of large enterprises have data scientists on staff, and virtually no small and medium businesses employ data scientists.
What this means is that at most companies, effective data management is the responsibility of any and all IT employees that these companies have on staff, including (especially) the DevOps engineers who bridge together development and IT operations. If the DevOps team doesn’t take responsibility for data operations, it puts at risk the applications that depend on healthy data flows to do their jobs.
That begs the question: How do DevOps engineers (most of whom weren’t trained in the intricacies of data management) learn to support data operations? Keep reading for tips.
Data Operations and Software Delivery
Data operations (DataOps) refers to all of the processes and workflows required to collect, store, manage and analyze data.
Traditionally, DataOps were siloed away from software delivery and management. The people who wrote, deployed and monitored applications were different from those who dealt with an organization’s data. That approach worked well enough in previous decades, when the data that organizations had to manage was less massive and less valuable.
Today, however, data has become too critical a component of effective software delivery to be left in a silo. Many modern applications depend centrally on data. If your app includes a predictions engine or a recommendations feature, it requires data to do its job. Similarly, the monitoring tools that IT teams use to assess application health are driven by data. Data collection and analysis are critical for security operations, too.
Thus, modern application delivery pipelines depend not just on the smooth flow of code from development into production, but also on a healthy flow of data. You can no longer isolate DataOps from application delivery (and even if you could, you probably don’t have a dedicated team of data scientists to handle your data anyway, as noted above).
Adding DataOps to DevOps Software Delivery
How do you actually go about integrating DataOps into an application delivery pipeline in a DevOps organization? Consider the following pointers.
Automate, Automate, Automate
Automation is just as important for effective DataOps, as it is for DevOps software delivery. By embracing tools that automate data collection, management and analysis, IT teams ensure that DataOps flow as smoothly as the software delivery pipeline.
What’s more, data automation tools can help reduce the amount of DataOps work that general IT staff need to perform. That’s a key benefit for DevOps engineers who don’t have extensive DataOps experience.
Another way of reducing the amount of DataOps work imposed on IT staff who are not trained as data specialists is to leverage AI to help them make decisions within DataOps workflows.
Until just a few years ago, only a few tools used AI in a practical way to power DataOps. That is now changing, and modern data management tools make effective use of AI to reduce the decision-making burden on human engineers.
Focus on the Data that Matters
Within the context of software delivery (and elsewhere), some data have more value than other data. Since you have finite staff and material resources to devote to DataOps, identifying which data is most important for your applications is critical for optimizing your DataOps workflow.
In other words, just because you collect data doesn’t mean you necessarily need to store and/or analyze it.
Be Infrastructure- and System-Agnostic
It can be tempting to rely on the data management tools provided, along with the cloud infrastructure you use or the platform you run on. However, although these tools might be helpful to a limited extent, relying too heavily on them puts you at risk of being locked in and having to rebuild your DataOps from scratch if you migrate to a new infrastructure or platform.
For that reason, choosing DataOps tools that work with any environment is a wise idea.
Educate Your Team
The final pointer for helping your DevOps team do DataOps is simple: You need to educate them about data and what makes it work well. Don’t expect to turn them into data scientists, but they should understand concepts such as data quality, and recognize the bottlenecks that can get in the way of healthy data performance. Although these concepts may seem basic to someone trained in data management, they are usually foreign to DevOps engineers.
To say that we live in a data-driven world is a cliché. But it’s a cliché that is eminently true if you work in software delivery. In many cases, you can’t do DevOps without having healthy DataOps, too. That fact, combined with the lack of dedicated data teams at most companies, means that DevOps engineers must learn to integrate DataOps into their workflows.
This sponsored article was written on behalf of Unravel.