Today's DevOps ecosystem is remarkably diverse. DevOps practitioners and companies are working to automate the full suite of IT functions and tools that today represent manual bottlenecks on software development, promoting increased developer independence and accelerated code delivery.
These tools span a wide range of functions, from application virtualization to performance monitoring, and organizations undergoing a DevOps need to consider which such collaboration tools (or collaboration software) will address their most substantial obstacles. However, they should pay special attention to data virtualization tools, an often-overlooked but crucial component of successful DevOps strategies. Data virtualization allows the large data sets associated with enterprise applications to be made available to developers, in a scalable, consistent, and self-service fashion.
Often, DevOps tools assume that code is king - and an application environment consists simply of that application and the OS it runs atop. But enterprise applications run on top of data, most commonly relational data stored in a database management system. Without a copy of this data, the application cannot meaningfully run, and that means that developers and testers need these copies to ship code at DevOps speeds. A data virtualization DevOps tool provides this capability.