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Data Warehouses

Data Warehousing and Data Mesh: Different Types of Goals

The world is full of different types of goals. Consider football. The goal in football is at the end of the field. A runner either crosses the goal or they don't, when trying to make a touchdown. Or, consider basketball. In basketball, when a player shoots the ball, the player’s shot either goes through the net or it doesn’t. Alternatively, consider ice hockey. When a hockey player shoots the puck, it either goes into the net — or it doesn’t.

Data modeling techniques for data warehousing

When setting up a modern data stack, data warehouse modeling is often the very first step. It is important to create an architecture that supports the data models that you wish to build. I often see people going straight to writing complex transformations before thinking about how they want to organize the databases, schemas, and tables within their warehouse. To succeed, it is key to design your data warehouse with your models in mind before starting the modeling process.

Why the Data Warehouse is Not Dead and Stronger Than Ever

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Five things you need to know about this topic: The data warehouse is the whack-a-mole of technology.

Integrating Your Data Warehouse and Data Mesh Strategies

Data warehousing requires data centralization, whereas data mesh enables a decentralized approach to data access. Organizations might think that the solution to their data management strategy requires a choice between the two, but the reality is that both approaches can and should co-exist.

Does the Data Warehouse Sit on a Single Physical Database?

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Five things to know about this topic.

BigQuery, Google's Enterprise Data Warehouse

The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in. By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills. Google Cloud Skills Boost provides real Google Cloud environments that help developers and IT professionals learn cloud platforms and software, such as Firebase, Kubernetes, and more.

Data Science Maturity and Understanding Data Architecture/Warehousing

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Data science is immature. This statement is not pejorative; it is simply a statement of historical fact. As such, it is not arguable.