Strategies for Enterprise-grade Azure-based Analytics

Over the past decade, big data implementations have been more sophisticated in particular for organizations operationalizing machine learning, analytics and data engineering. The pressures of data-driven cultures, multiple workload applications such as customer care, fraud management and cross-platform marketing are changing the game. Mixing machine learning with business processes and operationalizing analytics with data engineering practices places burdens on IT teams. Making these advanced data environments all work together is an ongoing challenge. While you can still “swipe and go” to implement data management environments in the cloud for an easy solution, the easy path is often littered with additional costs, higher overhead in terms of maintenance and synchronization. Data savvy organizations are taking a more measured and coordinated approach to their machine learning, analytics and data engineering infrastructures. These proactive approaches speed adoption among business stakeholders and lower administration and governance issues for technologists. Join John L Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Nik Rouda, director of product marketing at Cloudera, to discover how the world of cloud implementations have changed for the better and the future of an enterprise grade cloud environment for your organization using the right resources. Attend this webinar to learn about: Drivers for implementing machine learning, analytics and data engineering with a proactive approach Pitfalls associated with “immediate gratification” implementations How business stakeholders benefit from proactive approaches How proactive implementations improve the workloads of technologists Examples of real world customer implementations