What emerging cloud technology will prove to be the next big thing? Although it is always difficult to predict with accuracy, we can look at trends gaining traction within larger organizations and enterprises to get a pretty good idea about the next significant technological developments.
One of those new trends emerging within large enterprises is a practice referred to as DataOps. But before we look at this new trend, let’s look at the definition of DevOps.
DevOps is an acknowledged IT practice that is designed to maximize automation and repeatability in the building and deployment of applications. The goals of DevOps include a faster time to market, continuous application delivery, and agility.
One trend impacting nearly all enterprises today is the increasing importance of using data to drive value. Data is the primary way a company can gain a competitive advantage regardless of the industry in which they operate. Data is used to improve the customer experience, increase operational efficiencies or generate new sources of revenue.
Why Data Matters
A major shift in focus for IT is how to manage and deploy data-intensive applications. Where DevOps focused on lightweight applications, there is a completely new list of considerations that need to be dealt with when we begin to work with data-intensive applications.
Data management practices are central to the entire application lifecycle. Developments in machine learning and data science applications require vast amounts of training data. Deployment raises other issues because data-intensive applications need to take into consideration the concept of locality. Processes need to be deployed near where the data is generated to meet performance expectations. Because data is used by different groups within an organization, the access to that data, IT needs to provide access control and governance.
DataOps For Data-Driven Applications
We are beginning to see shifts in the types of infrastructure, platforms, and tools used to support DataOps. Some of the tools like containers and virtualization that support DevOps are also central to DataOps, but additional needs are driving the use of newer technologies.
DataOps will require a data fabric capable of providing access to massive volumes of data while at the same time having the ability to manage different types of data, including legacy structured data as well as unstructured and streaming data. With the development of a global data fabric, IT will be able to manage data across multiple physical locations and process it with huge stores of computing engines, including containerized processes. Another feature you can expect as part of a dataOps platform is the ability to optimize for data locality.
The Next Generation Of Market Winners
So, who will the winners be moving forward? It is probably safe to assume that the data center of the past decade will look quite different from data centers moving forward. As DataOps practices become more common and standardized, we will see a shift in the technology marketplace. Marketplace winners will be companies that deliver tools and platforms that cater to making the development and deployment of data-intensive applications easier and faster.