The data warehouse in the cloud is now a real possibility. It can be built on AWS, Azure, or GCP. It can be combined with onsite systems in a hybrid environment, but how do we understand this vision and make it real?
Join us to discuss this new landscape, a revised approach to building it, and how to tap into its flexibilities to scale up and scale out for machine learning and data science. We will discuss this new system and the new data architecture approach and thinking that it requires.
You Will Learn
- Cloud fundamentals such as storage, compute, and networking architecture
- CPU, GPU, in-memory, and disk architecture
- Cloud platform components, including infrastructure, analytics, and other technologies
- Business analytics, edge analytics, streaming analytics, and workflow
- Machine learning, deep learning, and their evolutions
- Security, privacy, multi-tenancy, and associated skills
- Risks and pitfalls and how to avoid them
- A suggested road map and checklists
- All with a knowledge of data warehousing, infrastructure, applications, and delivery