DataOps is the new data paradigm, it is an automated, process-oriented methodology used by
analytical teams and data engineers that streamlines the production of high-quality data
insights with security and control in the data flow, since the data volume it grows
tremendously every day, causing companies to innovate in their data systems, but most of the
time incorrectly, causing inefficiency in meeting market demand requirements. This
methodology merges with the Devops methodology, which will improve software
development and IT operations such as speed, quality, predictability and scale of engineering
and software deployment with a good team with strategies designed entirely in governance,
prioritizing always the customer's security and privacy. The objective of this work is to
bibliographically inform this model of revolutionary agile methodology with the collection of
information for "What it is", what are the steps, requirements for building an environment,
and how is it to form a DataOps team, addressing how it works , its tools, advantages, its
differences with DevOps and contribute to the incentive for companies and professionals in
the area of Information Technology in the implementation of this practice, carefully
presenting DataOps as a new agile practice model for companies to understand the
importance of using analysis of data in the corporate strategy in the form of a theoretical
framework, also promoting the search for more subjects of this practice that are so few in
Brazil.
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