Today’s business environment requires the use of data and analytics to compete effectively. With continuous advances in data and analytics technologies and capabilities, organizations often struggle to keep up with the changes and to manage data for maximum value and impact. Nearly every organization today is facing the need to rethink and refresh data architecture, yet most continue to work with turn-of-the-century architecture from the BI era. Patching new components onto the surface of legacy architecture—a band aid and duct tape approach—is not sustainable and does a poor job of supporting modern analytics use cases. Still, many avoid stepping up to modern data architecture because it is complex and difficult. The challenge is two-fold: to clearly define needed business and data capabilities, and to determine how best to weave new capabilities into existing data management practices.

This 4.5 hour course explores modern data management challenges, describes modern practices and data architecture design patterns, and describes a step-by-step process to get from business requirements to a modern data management architecture that is sustainable and adaptable to the future changes that are sure to come.

You will learn:

• The reasons that legacy data architectures need to be modernized
• The multitude of requirements for effective analytics data management
• The similarities and differences of Data Lake, Data Fabric, and Data Mesh architectures
• Techniques to identify analytics business capabilities and requirements
• Techniques to identify analytics data capabilities and requirements
• How to apply architectural design patterns and frameworks
• How to adapt reference architectures
• The path from requirements to a well-designed architecture
• Six techniques for architecture implementation

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