Data Modeling: Power BI Data Modeling Essentials

Explore normalizing denormalized data in Power BI with Power Query by creating four tables—sales (fact) and dimension tables for customers, products, and locations.

Explore normalizing data in Power Query by using a single source for multiple queries, removing unused columns, and removing duplicates with M code that selects columns from the orders table.

Learn to normalize dimensions in Power Query by converting multiple steps into a single, distinct query that selects columns and removes duplicates for customers and products.

Organize queries in Power Query by creating folders for orders, sales, customers, products, and locations, classifying them as sources, facts, and dimensions, then load only the needed tables.

Confirm and visualize relationships in Power BI data modeling by mapping fact to dimension tables in model view, using fit to page and relationship lines.

Explore Power BI data modeling essentials: many-to-one cardinality and single cross-filter direction, where the dimension filters the fact. Avoid scenarios where both tables filter themselves for accurate analysis.

Identify cardinality and cross-filter direction in Power BI by examining the relationship line between customers and sales, noting many-to-one, one-to-one, and one-way cross-filtering from dimension to fact.

Explore cross filter direction in Power BI by showing how a single-direction filter lets the customers table query the sales table, enabling accurate reports and slicer-driven insights.

Switch from single to both cross filter directions to let the sales table filter the customers table, revealing orders and corresponding customer details.

Explore cross filter direction in Power BI by linking customers, sales, and products through a relationship, adjusting direction from single to both to enable indirect filtering and then reverting.

Explain how Power BI favors a star schema over snowflake schemas, detailing fact and dimension tables, their relationships, and how pricing codes link to pricing tiers in a sample dataset.

Convert a snowflake schema to a star schema in Power BI by merging related dimension tables in Power Query and loading a simplified, dimension-first model.

Create a date dimension table to enable month, quarter, and year reporting without redundancies, using Power BI's DAX time intelligence functions and a complete date set.

Create date tables in Power BI with DAX, using calendar auto to auto determine ranges, and include standard columns like date, year, quarter, month, and month number.

Create a date table in Power BI using CALENDARAUTO, rename it to calendar, and connect it as the date dimension in the star schema, with optional fiscal year settings.

Mark the date table in Power BI, relate it to the fact table via order date, and use ship date as an inactive alternative for DAX reporting.

Create a year column in your date table using the DAX year function in Power BI, using the new column button and calendar table, while aligning active and inactive relationships.

Learn to use DAX date functions to create quarter and month number columns from the calendar date in the calendar table, using year, quarter and month inputs.

Create a month name column from calendar date using the DAX FORMAT function with 'mmmm', demonstrating locale options to show names in French, Spanish, or Yoruba.

In Power BI, ensure your date table includes month name and month number, then sort the month name by the month number to achieve natural January–December order.

In Power BI data modeling, fix day-of-week sorting by creating a day-of-week number with the DAX weekday function and setting the visual to sort by that column.

Create a week of year column in Power BI using the week name function to return week numbers from the calendar date, with a start day and a date table.

Learn to create date tables in Power Query with the M list.date function, starting January 1 of the first year, to ensure complete Power BI calendar coverage; compare with DAX.

Create a Power Query date table by extracting order and ship dates from the orders table, combining them into a date list, and naming the step as data date.

Power Query dates: extract the first and last dates from the data date list using list.min and list.max, then rename steps as first data date and last data date.

Set the date table bounds using the first and last data dates; apply date start of year for the beginning date and date end of year for the ending date.

Identify the first and last dates to define the calendar, then compute the end minus start date. Convert the duration to a number with number.dot prom and add one.

Create a duration step in power query to count days between dates, then generate a date table from a start using list.date and add year, quarter, month, week, and day.

Learn how to model with multiple fact tables in a single Power BI model, sharing dimensions across sales, inventory, and the target data set while handling exclusive dimensions.

Import the target data into Power BI desktop, convert year and month into a single date column, and relate it to the calendar table for reporting.

Learn to replace many-to-many relations with a bridge table by creating a unique customer segment list and linking it to targets and customers for accurate Power BI reports.

Learn to troubleshoot Power BI data models by mastering cardinality, primary and foreign keys, and single-way cross-filter direction across two fact tables (actual sales and targets) with dimension tables.

Troubleshoot by ensuring an active relationship between products and actual sales using product ID, set the correct cardinality, and recreate the model layout to resolve identical values across categories.

Identify and fix issues where no report categories display by checking and correcting relationships, cardinality, and cross filter direction between fact and dimension tables in Power BI data modeling.

Troubleshoot blank date categories by verifying the relationship between the fact and calendar tables and converting a date time column to date only in Power BI.

Diagnose why a Power BI visual shows identical totals across locations due to missing relationships, then learn to add related tables, set 1-to-1 cardinality, and troubleshoot ambiguous path errors.

Troubleshoot Power BI data modeling by identifying bidirectional cross filters, then change cardinality to many-to-one and set cross filter direction to single between actual sales, locations, and groups.

Learn to show items with no data in Power BI visuals and interpret blank categories caused by mismatches between fact and dimension tables.