The rapid proliferation of low-cost or open-source cloud-native analytics services has whetted the appetites of a broad array of citizen data analysts eager to try out a variety of machine learning and AI techniques. The simplicity and ease-of-use of analytics tools and services have lowered the bar for data consumers with little prior training to test out advanced analytics techniques.
However, the complexity of the data analytics lifecycle (from data acquisition and ingestion to management, engineering, and provision) and the configuration of the analytics environments still pose challenges to fully empowering data analysts. In this talk we discuss how modern cloud service vendors provide tools and guidance for developing templatized computing+data platform patterns. These patterns can be used to automate the launching of customized computing instances that are preloaded with data and have an analytics product stack that enables the data analysts yet shelters them from the complexity of platform design and implementation.
Attendees will learn about:
- Assessing data consumer needs
- Identifying and configuring data resources
- Initiating the appropriate tool stack
- Encapsulation of the platform template
- Associated cloud resource management issues