Bring your data architecture in to the 21st century, one step at a time.
Traditional data warehouse architectures strain to meet the challenge of modern-day BI and analytics. Development teams can’t keep up with the demands of information-hungry knowledge workers. Yet all too often, architects “solve” the problem by adding a single box to their architecture diagram—one labeled “Hadoop.” The result is chaos and confusion.
In this course, you will learn the right way to architect a data management platform—one that is responsive to the needs of your business. Such a platform connects your architecture to the drivers of business impact through a capability-driven framework for BI and analytics that acknowledges OLAP, reporting, analytics, governance, data quality management, self-service solutions, and more.
Learn how to map out a modernized data architecture first and then map it to relevant technologies. Learn about non-relational data management platforms, the changing role of the data model, the place of virtualization, new strategies for metadata, adapting to changing development techniques, and how to cope with self-service demands.
You Will Learn
- How to plan and document a modernized data architecture
- Techniques to map standards and technology to architecture layers
- How non-relational data platforms fit into new data architectures
- The role of virtualization and cloud storage in your architecture
- The changing role of the data model and metadata
- How modern data pipelines support data integration requirements
- How to adapt architecture to fit modern development techniques
- How data architecture adjusts to support self-service BI and analytics
- This course is intended for anyone who contributes to data mart architecture, requirements, or development, including technology leadership (including CDO/CAO), BI and analytics program leadership, data architects and data modelers, data engineers and database administrators, and BI and analytics solution developers