Building a road map for modern and cloud-based data and analytics requires new ways of thinking about platforms and pipelines with services to fully leverage the value the cloud has to offer. It is time to refresh not only the architecture to support multiple analytics capabilities but also the core elements that unify your data strategy and road map. The unified data and analytics architecture delivers on business demands for data warehousing, self-service data analytics, and data science with a unified data lake foundation and user-driven collaboration, governance, and semantics layer. Cloud and hybrid architectures facilitate modern data capabilities but must be approached within the context of a broader data strategy.
Rooted in understanding the principles and frameworks first, architects can assess their current architecture and develop a modernization strategy that advances technology, fills current gaps, and plans for new skills and roles necessary to manage an enterprise-class data environment. This tutorial includes recent updates on features within AWS, Azure, and Google cloud services for analytics architectures.
You Will Learn
- A conceptual architecture for enterprise analytics capabilities
- A logical reference architecture for optimizing data platforms, engineering, and analytics
- Prioritizing capabilities when evaluating vendors in data unification and data virtualization
- Top five mistakes to avoid in cloud migration for analytics
- Building a road map for cloud migration that aligns with your data strategy