Powering DataOps with Control-M Workflow Orchestration

Considering that almost 85 percent of big data projects fail, it’s no surprise organizations are adopting DataOps to derive value from data-driven applications and analytics. A successful DataOps program relies on solving the production challenge of operationalizing complex workflows. This session demonstrates how data scientists and engineers can streamline the delivery of data-centric digital services by accessing Control-M production-ready orchestration of cloud services with Python, the preferred programming language among data professionals.