The software development world has long accepted DevOps, a popular methodology that brings development and operations teams together to make software development and delivery more efficient by implementing processes and practices like Agile development, continuous improvement, continuous deployment and small, well-controlled development sprints. But in the database community, DevOps is not well known nor frequently applied. 

DataOps, on the other hand, is more focused on improving communication, accelerating integration and breaking down organizational barriers that impede the flow of data between data managers and data consumers throughout the organization, especially in the service of data analytics. While DataOps includes the Agile methodology and some other processes from DevOps, it is an independent approach to data analysis.

So, what is a well-governed database management team to do? Use only DevOps? Or use only DataOps? In fact, DBAs, SREs and data analytics teams will want to use elements of both. In this session, we will:

1. Discuss the risks and challenges faced by organizations that do not implement policies and procedures to integrate development, operations and data.
2. Align our understanding of DevOps and DataOps with the well-regarded Information Management Maturity Model discussed by Gartner.
3. Review the principles of DevOps and DataOps, paying special attention to those patterns and practices that are most impactful for database, data and analytics professionals.

With these lessons in hand, organizations can shift their database management and data integration experiences from unreliable, inflexible, slow-moving and error-prone to stable, reliable, predictable and highly scalable. In almost every case where an organization outperforms its peers, it has applied and uses these practical and powerful methodologies to excel-and you can, too.