TDWI San Francisco Seminar

Prerequisite: None

John O'Brien

President

Radiant Advisors

Leading the way in new data integration best practices, data streaming hubs and data pipelines are gaining adoption for their proven scalability, agility, and improved manageability. Database replication is a proven technology that has a new role in data ingestion to quickly capture operational data in near real-time and for changed data capture in cloud data lakes and analytics pipelines. Implementing these data engineering concepts and organizational teams delivers agility in data analytics projects.

Data unification is a critical facet in data-centric organizations that empower their people with data, and data unification strategies accelerate cloud adoption and increase confidence for BI development, self-service data analytics, and data science projects. As the number of data stores increase, consolidate, and evolve during a cloud migration, business analysts and data scientists need to find data, understand data, prep data, collaborate, and publish data in a governed user-driven way. As critical components of data unification, data virtualization, semantic layers, and data services have become a “data marketplace” of sorts for users, within the parameters of essential governance and enterprise manageability.

You Will Learn

• How streaming data hubs work in cloud and hybrid cloud architectures
• How database replication patterns are important to data lake ingestion and data pipelines
• How to engineer data pipelines in batch and streaming processing
• The six main components of data unification, including data catalogs and data prep
• How data virtualization works and its role in data unification strategies
• How semantic layers benefit data governance, self-service, and data science

Geared To

• 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