Star-Schema

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 warehousing concepts for analysts Data warehouses bring together data from multiple sources to support analysis and reporting. For analysts, it is a trusted base where questions can be answered consistently across teams and time periods. Clean, well‑organized data helps you spot trends, measure performance, and tell a clear story with numbers. Core structure and flow Staging area: raw extracts arrive here to be inspected. The warehouse: integrated, cleaned data ready for analysis. Data marts: smaller, focused views for specific teams like sales or finance. This flow keeps raw data separate from what analysts actually use, which reduces confusion and speeds up reporting. Modeling ideas ...

Data Warehousing and Business Intelligence Data warehousing and business intelligence (BI) work together to turn raw data into clear insights. A warehouse stores clean, organized data from many sources so teams can answer questions quickly and reliably. What is a data warehouse? A data warehouse is a central store designed for analysis, not day-to-day transactions. It keeps historical data, runs fast queries, and supports questions like “What were our top products last quarter?” ...

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. ...