Dimensional-Modeling

Data Modeling Techniques for Business Intelligence Data modeling is the backbone of reliable BI. A well-designed model helps analysts combine data from sales, marketing, and operations to spot patterns. It also makes dashboards faster and reports easier to read. In this article, you will find practical data modeling techniques that fit real projects and teams of different sizes. Start with business questions Begin by listing the questions business teams want to answer. This defines the facts people care about and the level of detail. Keep the scope tight and shareable. A clear business question helps avoid overbuilding the model. ...

Data Warehouses and Data Marts for Analytics Data warehouses and data marts are two common ways to organize data for analytics. A data warehouse stores integrated data from many sources in a central, consistent schema. A data mart is a smaller, targeted slice of data designed for a specific group or line of business. Together they help teams ask questions, track trends, and make better decisions. Both help turn raw data into insights, but they differ in scope and goals. Key differences include: ...

Data Warehousing Concepts for Analysts A data warehouse is a stable, integrated source of truth for reporting, dashboards, and data exploration. It collects data from many systems, cleans it, and stores it in a consistent format. The goal is faster, reliable decisions across teams. Core ideas to know include how data is modeled, how it moves, and how it stays trustworthy. Dimensional modeling divides data into facts (measures) and dimensions (descriptors). The common designs are star schema, which keeps tables wide and simple, and snowflake schema, which adds normalization for some dimensions. ETL and ELT describe when transforms happen: ETL transforms before loading; ELT pushes transforms into the warehouse after loading. Data quality and governance cover accuracy, lineage, and access controls to protect the data and the people who use it. ...