Developing a data architecture used to be straightforward. Now lowered expenses and simplified management are inspiring senior managers to migrate many organizations’ enterprise reporting and analytics infrastructures to the cloud.



Cloud service providers are rapidly producing a dizzying array of storage, computing, and other value-added services. Enterprises supplanted old-fashioned extract, transform, and load (ETL) processes with data onboarding and pipeline orchestration. With multicloud hybrid architecture
only getting more complicated, even highly trained technical people have trouble keeping track of what’s what.

Organizations increasingly need to support more sophisticated data scientists as well as a broader community of citizen data analysts—those business data consumers and business analysts who maintain a level of data awareness and are comfortable using end-user technologies coupled with self-service data access to produce their own reports and analytics. Both must be enabled without creating additional IT bottlenecks. TDWI wrote this Checklist Report to raise awareness among various corporate personas about cloud data warehouse
architectural paradigms.


We provide some straight talk about the current state of data warehousing, and answer questions such as:

  • What is an enterprise data warehouse?
  • What is the difference between a data warehouse and a data mart?
  • Who is using a data lake and why?
  • What is a data lakehouse?

Critically, more chief data officers and senior information management directors are compelling their teams to migrate their data warehouse environments to the cloud. Although there are many benefits to a modernized data warehouse environment in the cloud, the devil, as always, is in the details. This checklist discusses emerging platform paradigms that complement the traditional data warehouse and explains how enterprises can deploy these components across a hybrid cloud architecture.

Learn more: data processing