Data warehouses have changed a lot from where they began, too. To get to know things better, here is an overview of modern data warehousing.
'Modern data warehouse' is an important term for any enterprise dealing with data and wants to ensure integrity, accuracy, and reliability. Without the application of data warehousing, companies are forced to depend on accessing raw data stored within applications in a human-based (meaning, more accuracy issues), slow process. Companies can often run massive data analytics and store, process, and make it accessible for authorized users when needed with data warehouses.
Data warehouses have changed a lot from where they began, too. To get to know things better, here is an overview of modern data warehousing.
Defining a Modern Data Warehouse
It isn't easy to define something that is constantly changing. The technology we see today might not be what we see tomorrow, and the same is the case for data warehousing. Data warehousing has been around since the 1980s, and with the rise of big data and such, it has changed a lot. In simple terms, modern data warehouses can be defined as a central data management system storing and consolidating data to support analytics, machine learning, data mining, etc., within an organization.
We are also particularly fond of the way author Martyn Jones defines modern data warehouse and believe that it holds true even with the evolution: “A data warehouse is essentially a business-driven, enterprise-centric and technology-based solution. And it is used to provide continuous quality improvement in the sourcing, integration, packaging, and delivery of data for strategic, tactical and operational modelling, reporting, visualization, analytics, statistics, and decision-making.”
