December 16, 2021
Organizations bent on deploying a strategy for digital transformation are rapidly transitioning their data and applications to the cloud.
Cloud modernization has become the rallying cry for organizations looking to take advantage of advanced analytics using machine learning and artificial intelligence while continuing to support traditional data warehouse consumers.
Data consumers with different needs and expectations have become more sophisticated in the use of analytics and data science tools, and their expectations for data democratization have created demand for a simplified way to access data assets shared across the enterprise without the constraints imposed by the traditional data warehouse architecture.
This TDWI Checklist examines the limitations of existing data architecture strategies when supporting emerging and future analytics needs and how a data lakehouse approach addresses those limitations.