Myth-Busting DataOps: What It Is (And Isn’t)
What is DataOps and how does it fit into the modern enterprise?
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.
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.
