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
- 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