This page is going to cover some of the most common types of data modeling techniques we see used by modern analytics teams (relational, dimensional, entity-relationship, and data vault models), what they are at a high level, and how to unpack which one is most appropriate for your organization.
Datawarehousemodeling is the process of designing and organizing your data models within your datawarehouse. Learn the modelingtechniques you should know.
In this guide, we’ll break down what datamodelingfordatawarehousing means, why it’s essential, common techniques, and we’ll walk through examples to make concepts clearer.
Datawarehouse schemas structure data into fact tables (numeric metrics) and dimension tables (descriptive attributes). The three core models are: star schema (denormalized for speed), snowflake schema (normalized for storage efficiency), and galaxy schema (multiple interconnected facts).
There are several common (and less common) approaches to modelling data in a data warehouse. In this article, we’ll look at seven key modelling techniques, weigh their pros and cons, and help you choose the right approach for your data warehouse.
In this article, I will provide an in-depth overview of datamodeling, with a specific focus on Kimball’s methodology. Additionally, I will introduce other techniques used to present data in a user-friendly and intuitive manner.
There are several different data modeling techniques that can be used in the context of data warehousing. The four most commonly used techniques are the Inmon methodology, the Kimball methodology, Anchor modeling, and Data Vault modeling.
In this modern datawarehouse guide, we will study datawarehousemodeling and simplify the topic. Whether you are new to data or just researching better warehouse optimization, this piece will provide practical insights to help you make the most of your data.
Datawarehousemodels define how information is structured inside a datawarehouse. They determine which tables exist, how they connect, and how queries are executed at scale, making them a core element of any datawarehouse architecture.
