DevOps is shifting from being a niche approach to application development and deployment, and move into the mainstream world of production applications & infinately scalable infrastructures.



Given any IT needs automation both for data as well as infra assets it makes sense to employ DevOps best practices to help implement a continuous cycle of innovation while achieving your Big Data demands.

DevOps toolchain already exists to support Big Data tools & processes its apparent that most of us see the benefits already , right from process to collect data, to data filtering  to horizontally scalable infra to analytics. the entire pipeline is becoming more efficient to deliver best possible Big Data solutions.

Until recently, DevOps was employed mainly by cloud providers or early adopters in the financial services or telco worlds simply wanting to experiment.

Now adoption is starting to spread more widely, at least in pockets, if not on an enterprise-wide basis, into other sectors ranging from manufacturing and retail to pharmaceuticals and life sciences.  

this has not only created new roles like DevOps Data Engineer or DevOps Data Architect those are eager to work on the research, design, and implementation of new technologies which will provide a solid ground for Data Processing and Machine Learning tools.