Скачать или смотреть 🎬 Episode 3: Power BI Data Modeling Made Easy! (Fact Tables, Schemas, & More)

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Welcome back! In this video, we're diving deep into the world of data modeling in Power BI 📊. It's a crucial step that ensures your reports are fast, accurate, and can handle a lot of data.

Fact and Dimension Tables -

First, let's talk about the two main types of tables you'll work with: Fact Tables and Dimensional Tables.

Fact Tables are all about the numbers 🔢. Think of them as your core data tables containing all the measurable, transactional information like sales amounts, order quantities, and timestamps.

Dimensional Tables give context to those numbers 🗓️. They're your lookup tables with descriptive attributes like product names, customer details, or dates. They provide the "who, what, where, and when" for your fact table.

Star vs. Snowflake Schema

Next, we'll look at how these tables can be arranged. We have two main schema types:

The Star Schema is the superstar of Power BI ✨. It's a simple, denormalized model with a central fact table connected directly to its surrounding dimension tables. This structure is super fast and easy to understand.

The Snowflake Schema is a more complex, normalized version ❄️. It's like a star schema where some of the dimension tables are connected to other dimension tables, creating branches. While it saves some space, it's generally slower and more complicated to work with in Power BI, so we recommend sticking with the star schema for the best performance.

Relationships and Cardinality
Finally, we'll build the connections between our tables in the Model view 🔗. The most common type of relationship you'll see is a one-to-many relationship, where a single value in your dimension table (e.g., one customer) relates to many values in your fact table (many orders from that customer).

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What about those tricky many-to-many relationships? Power BI doesn't handle them well directly, but there's a simple solution: a bridge table 🌉. You can create a bridge table using a simple DAX formula to connect the two tables and resolve the complexity.

0:00–2:30 – Introduction to data modeling in Power BI; Fact vs. Dimension Tables
2:31–5:30 – Star Schema (central fact table, connected dimensions); Snowflake Schema (normalized, more complex
5:31–10:00 – Relationships: one-to-many, primary keys (in dimensions), foreign keys (in facts)
10:01–13:00 – How to import & create tables (Excel, DAX, Enter Data)
13:01–17:30 – Creating a dynamic calendar table in Power BI (using DAX)
17:31–26:00 – Why correct relationships make visuals interactive; troubleshooting unresponsive visuals
26:01–32:00 – Practical: Building and organizing relationships in Model view


That's it for today's video! Master these concepts, and you'll be on your way to building robust and scalable Power BI data models.

The fuel bunk dataset consists of the fact tables namely dip, stock, shift reading, fuel rate, cash and expense tables for- https://drive.google.com/drive/u/1/fo...

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