Myth-Busting DataOps: What It Is (And Isn’t)
What is DataOps and how does it fit into the modern enterprise?
- By Judy Ko
- February 7, 2022
DataOps is garnering some well-founded hype right now. With all the noise generated by a groundbreaking discipline, it can be challenging to understand precisely what DataOps is and how it fits into the modern enterprise.
Let’s start by defining the term: DataOps is a set of practices and technologies that operationalize data management and data engineering to deliver continuous data for modern analytics in the face of constant change. The three most important terms in that definition are:
Operationalization: This means building operational manageability and resilience into your data processes so they can withstand the dynamic environment and high-pressure demands of an enterprise.
Continuous data: The pace of business in a digital, post-COVID 19 world is relentless, and users need access to data continuously. This doesn’t just mean access to real-time data streams; it also means having immediate access to any new sources of data that emerge.
Constant change: The days of a centrally planned and carefully change-managed IT infrastructure are long gone. With the ready accessibility of the cloud, data and systems are changing on a monthly, weekly, or even daily basis -- often without notice. This means your practice has to assume things will change unexpectedly and be able to handle that change gracefully.
DataOps tackles these issues head on, making it the sustainable way for enterprises to scale up modern data architectures. More than that, it turns these challenges into business value. Below, three common misconceptions about DataOps are explained.
Myth #1: Traditional data integration can support DataOps
Traditional data integration is not designed for a DataOps world. In fact, antithetical to DataOps, the conventional data integration approach cements assumptions about architecture and infrastructure so even small, innocuous changes can bring the flow of data to a grinding halt.
Traditionally, data flow between data producers and consumers (for example, business analysts) is achieved by developing a mapping with a data integration platform. With traditional data integration, data engineers must fully understand the finer details of data sources and destinations at all times. This quickly becomes unmanageable across the hundreds of applications and systems in an enterprise.
New things are constantly happening across this data supply chain, and data engineers cannot keep up with them on their own. Any small change to any source or destination, such as a version upgrade or a data type change, can cause disruption and pose a significant accuracy risk leading to data loss or data corruption. Worse, users could even be working with undiscovered and untraceable incorrect data for who knows how long!
At StreamSets, the real difference we see between DataOps and the traditional data integration model is the level of control each gives data engineers to manage change. Changes to data structure, semantics, and infrastructure are never-ending in the enterprise. Traditional data integration would have data engineers try to keep up with that change manually -- an impossible task that leaves teams overworked and overwhelmed. DataOps automates and streamlines the process as much as possible, leaving data engineers to the important work of building new data pipelines and delivering continuous data.
Myth #2: DataOps adds complexity to the enterprise
As a term with a nebulous definition, DataOps can sound complicated. It’s quite the opposite. DataOps reduces complexity in the enterprise by incorporating DevOps principles to enable automation and monitoring across the full life cycle of productivity. DataOps technologies are also used to build systems that are resilient to change and enable self-service for those who best understand an enterprise’s data needs. By operationalizing the data pipeline life cycle, businesses empower data engineers to scale and rapidly integrate systems while also bolstering the stability and resilience of data pipelines created.
By training your data engineers with a DataOps mindset, you can overcome the flaws of traditional data integration and reduce friction, streamline with automation, make humans’ jobs easier, and drive better business outcomes.
Myth #3: DataOps is not ready for prime time -- it’s something aspirational to strive for
Make no mistake: DataOps is very real and within reach of the enterprise today. Many organizations (from Humana to IBM to Shell) already harness the power of DataOps to accelerate business. Instead of looking at DataOps as a goal to strive for, look at it as a way to achieve automation and performance goals.
Below are three actionable steps for building a DataOps practice within your enterprise:
1. Empower your data engineering team to use a true DataOps platform. Insulate the data engineering team from the details and nuances of the domain-specific as well as technological aspects of your data producers and consumers.
2. Build a center of excellence using the power of DataOps. With a center of excellence (CoE), data engineers can build the skills and knowledge to provide rapid integration and support for unfettered communication between data producers and consumers. Plus, because a DataOps platform (such as StreamSets) abstracts away the vast majority of technical details, a small team of data engineers in a CoE can empower hundreds or even thousands of analysts and other data consumers to access the data they need in a self-service manner.
3. Enable data observability across your entire enterprise with a single pane of glass. If you do the first two steps right, you empower your enterprise with a single pane of glass to observe the workings of your data architecture across both on-premises and cloud environments, providing the observability needed for transparency and governance.
The Bottom Line
DataOps is here, and the time to implement it is now. Rather than aspirational itself, it’s a way to reach aspirational goals. By adopting a DataOps mindset, enterprises can help eliminate the bottlenecks and inefficiencies of traditional data integration while empowering all stakeholders across teams. With DataOps, organizations can reclaim the business agility and confidence from data that they deserve.
Judy Ko is the chief product officer at StreamSets where she is responsible for the DataOps platform that delivers continuous data in a multicloud world and the user experience that delights data engineers. You can reach the author via email or via LinkedIn.