Organizations that tend to consider DevOps as pure process maturity versus big data as technology stream tend to treat them in silos, leading to inefficiencies. DevOps' goal is to make software production and delivery more efficient, and including data subjects within the scope of continuous delivery processes to embrace DevOps will be a big asset for accomplishing organizations. Many IT leaders are now under increased pressure to produce results for investments in big data and data science projects. Big data projects are becoming more challenging. Applications are now showing up in big data projects forcing analytics scientists to revamp their algorithms. Major changes in analytic models cascades to revised resources and infrastructure in short duration. The entire process slows down if the operations team is kept out of the loop, negating the competitive advantage that big data analytics provide and warranting the need for DevOps collaboration.

In big data projects,...