How can you use Hadoop to implement a data warehouse?

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Data warehousing is a process of collecting, integrating, and analyzing data from various sources to support business intelligence and decision making. Traditionally, data warehouses are built on relational database management systems (RDBMS) that store structured data in tables and use SQL for querying. However, with the increasing volume, variety, and velocity of data, RDBMS may not be able to handle the scalability, performance, and flexibility requirements of modern data warehousing. This is where Hadoop, an open-source framework for distributed processing of large-scale data, can offer a solution. In this article, you will learn how you can use Hadoop to implement a data warehouse that can handle diverse and complex data sources, enable fast and parallel processing, and support various analytical tools and applications.