Creating an enterprise data architecture that can deliver analytics capabilities requires understanding the strengths and weaknesses of database technologies, their deployments, and the roles they perform. SQL database technologies have a variety of analytics optimizations and maturity while more recent NoSQL data technologies fulfill analytics needs beyond the RDBMS. Now database services built on cloud-native architectures take advantage of cloud’s elasticity and scalability.
This course is intended to educate attendees on these technologies and a methodology for creating data architectures based on polyglot persistence and other architecture principles. This knowledge will empower you to make the decisions necessary to build a hybrid or multicloud data architecture that encompasses your data warehouse, data lake, analytics sandboxes, data science platforms, MDM, and enterprise data hub needs.
You Will Learn
- Differences between SQL databases for analytics (MPP, columnar, in-memory)
- Capabilities of analytics NoSQL databases and appropriate use cases for each (graph, document, Spark, object stores)
- Options for cloud databases and managed services in Amazon Web Services, Azure, and Google Cloud Platform
- How to design an optimal hybrid/multicloud data architecture
- Enterprise architects, data architects, database administrators, technologists, analytics leaders, CIO/CDOs