Companies have an opportunity to realize the full value of cloud and hybrid-cloud adoption when they understand both emerging best practices and elements of cloud-native architecture. For many, lifting and shifting their current environment into the cloud is a known place to start, but it’s only the beginning of the transformation required. In order to maximize analytics capabilities, it is necessary to capture business strategy and priorities to design the architecture for tangible benefits in every step of the migration process.
Day one of this seminar begins with a view of relevant business initiatives and how to translate them into analytics capabilities and priorities. This provides the drivers for an enterprise data and analytics framework that addresses an organization’s maturity and gaps. The day continues with unbiased essential technology primers on cloud environments and database technologies for analytics and a logical reference architecture that includes data lakes, data warehouses, data hubs, analytics sandboxes, and data science platforms. From this, specific physical architectures and vendor technologies can be implemented as needed in AWS, Azure, or GCP as hybrid and multicloud architecture strategies.
You Will Learn
- How to translate business initiatives to data and analytics capabilities
- How to assess maturity and what needs exist for the enterprise data and analytics framework
- How cloud-native architecture and services benefit data and analytics
- An overview of SQL and NoSQL technologies for polyglot persistence
- The components of a logical data architecture for data streaming, data lakes, BI, self-service, and data science
- Physical architecture designs in Amazon Web Services, Azure, and Google Cloud Platform
- Enterprise architects, database administrators, data integration architects, data engineers, analytics leaders, CEO/CDO/CIOs, and anyone seeking to understand how to unlock enterprise analytics with cloud platforms