The volume of data generated in the world has increased exponentially, and in the last 10 years, it is no longer possible to store all the data generated. In 2020, about 40 trillion gigabytes of data were generated. But, after all, what does it matter in the academic world? What does this impact on the business of a small, medium or large company? How can this impact our life? The answer starts with the search for people with technical training to work in the processing of all this volume of data. They are professionals with the ability to translate information into strategic decisions, essential within companies, in research and innovation. At this moment, we are faced with a major problem: there are not enough people in the world with enough technical skills, today and in the next 10 years, to work in the areas of data and generate value with them. Why such an intense search for professionals who work with data? Companies have already understood that the data driven culture generates an increase in revenue, cost reduction and improvement in people's quality of life. And when we say “companies” we mean all market segments: industry, health, retail, finance, e-commerce, wholesale, among others. There are almost 20 million companies in Brazil alone. Imagine 1% of them hiring technology professionals, we are talking about 2 thousand vacancies, but we already know that the projection is 700 thousand vacancies from today to the next 5 years. With the information mentioned, the question remains about how the academic area, universities, faculties, technical institutes and schools can be structured to technically train all this volume of people. In addition, how to generate a greater bond between educational institutions and companies so that this process can be accelerated and optimized for people who intend to enter the job market. The idea of this seminar is to present how big data and analytics are being implemented in real cases in companies from different sectors. How Data Science, Data Engineering, Machine Learning Engineering, DevSecOps Engineering MLOps, DataOps and Cloud Architecture are being structured.