DevOps is a culture which energizes work place practices by unleashing the synergy of the Development and Operations teams working together as a whole. It results in shorter development cycles, increased frequency of deployment, with a high degree of dependency coupled with alignment to business needs.
Big Data consists of highly voluminous and complex data sets. Due to its immense size, the traditional data processing application software cannot cope with the volume. Big Data poses many challenges, among them are: Capturing and storing data, analysis, search, sharing, transfer, query, visualizing, updating methodology as well as privacy of stored information.
The main goal of Big Data is to increase the processing speed of the data from various sources such as mainframes, relational database management systems, flat files on a Hadoop cluster, Opensource software utilities. To crunch such data requires using Continuous Integration and Deployment or CI/CD patterns.
Big Data throws up a whole new set of challenges to IT Managers. The challenge posed by the analytical sciences portion contained in Big Data lead IT Managers to abandon DevOps. The team performing Big Data analytics in-house, formed a separate group which was again in a silo.
IT Managers soon found out that with this separation of functions between the Big Data analytics team and the DevOps group, the old problems of inefficiencies and bottlenecks kept cropping up. In fact, in some cases, issues were getting to be a major headache since some Big Data projects were more challenging and complex than originally anticipated.
The need for better efficiencies and results forced Big Data analytic scientists to revamp algorithms. Since such revamps required different infrastructure resources and the Operations team was kept out of the loop till the final stages, lack of coordination, delay in communication etc. lead to major headaches. This prompted a rethink which lead to the realization that DevOps is needed for Big Data. By combining both, Managers could access and analyze Big Data easily and gain valuable business insights and a competitive edge.
The CI/CD patterns of DevOps have to be made applicable to Big Data and this is achieved by the following method:
The following steps are carried out in Test Automation. It carries out a review of the quality of code and data as it flows through the pipeline.
a. Missing, truncation, mismatch of data,
b. Null, wrong translation of data
c. Misplaced data and
d. Extra records.
Integrating Big Data and DevOps will definitely throw up some challenges. The basic requirement is that the Operations side of the business must get acquainted with the Big Data platforms. Once the integration process is completed, it will give a huge competitive edge to the business.
DevOps is definitely for those aspiring IT professionals who want a challenging and rewarding career in the IT field. Get proper DevOps training and certification today.