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Power BI is a versatile tool that allows you to handle large amounts of data from various sources, automate data workflows, and perform advanced analytics using DAX expressions. It enables the creation of interactive reports and dashboards with top-tier visualization tools.

Power BI Desktop is divided into two main environments: the back-end and the front-end. The back-end houses the Power Query Editor, which is responsible for extracting, transforming, and loading raw data to the front-end using M code. Conversely, the front-end consists of the Data, Model, and Report views. This is where the majority of data modeling, analysis, and visualization are conducted using Data Analysis Expressions (DAX). Within Power BI Desktop, M and DAX serve as two separate functional languages. M is utilized in the Power Query editor, specifically designed for the extraction, transformation, and loading of data. On the other hand, DAX is employed in the Power BI front-end, specifically designed for the analysis of relational data models.

Here are some featured DAX functions that can be used in different scenarios: In the first case, if we want to see the percentage of returns by different products and product categories, we can use the CALCULATE function, which evaluates an expression in a context that is modified by filters. The ALL function can also be useful as it returns all rows in a table, or all values in a column, ignoring any filters that have been applied.

In the second case, if we need a measure that multiplies order quantity by product cost without adding redundant columns to our Sales table, we can use the SUMX function. This function allows us to loop through the same expression on each row of a table, then apply some sort of aggregation to the results. The RELATED function can also be used to return related values in each row of a table based on relationships with other tables.

In the third case, for month-over-month and year-over-year comparisons, we can use the DATEADD function which adds or subtracts a specified number of intervals from a date. The DATESINPERIOD function can also be used to generate a table of dates over a specified period.

In Power BI, cardinality is a fundamental concept in data modeling that refers to the uniqueness of data values contained in a particular column of a database. It plays a crucial role in determining the relationships between tables within the data model. Ideally, these relationships should adhere to a one-to-many cardinality. This means that for each unique instance of a primary key (which serves to uniquely identify each row in a table), there can be multiple instances of corresponding foreign keys. Understanding and applying the concept of cardinality is essential for effective data analysis and interpretation.

In Power BI, you may be dealing with various types of data. This could include time-series data, which is a sequence of data points indexed in time order. You might also work with geospatial data that is associated with geographic locations. Categorical data, which is often used in statistical analysis, could be another type you handle. Financial data, crucial for economic and business decisions, might also be part of your work. Textual data, which is essentially unstructured text, could be another type you deal with. Lastly, you might work with funnel data, which is often used in marketing to represent the customer journey towards the purchase of a product or service.

In Power BI, the type of communication you want to achieve can be categorized into four main types:

1. Comparison: This is used to compare values over time or across categories. The common visuals used for this purpose include Column/Bar Chart, Clustered Column/Bar, Data Table/Heat Map, Radar Chart, Line Chart (for time series), and Area Chart (for time series).
2. Composition: This is used to break down the component parts of a whole. The common visuals used for this purpose include Stacked Bar/Column Chart, Pie/Donut Chart, Stacked Area (for time series), Waterfall Chart (for gains/losses), Funnel Chart (for stages), and Tree Map/Sunburst (for hierarchies).
3. Distribution: This is used to show the frequency of values within a series. The common visuals used for this purpose include Histogram, Density Plot, Box & Whisker, Scatter Plot, Data Table/Heat Map, and Map/Choropleth (for geospatial data).
4. Relationship: This is used to show correlation between multiple variables. The common visuals used for this purpose include Scatter Plot, Bubble Chart, Data Table/Heat Map, and Correlation Matrix.

For a Power BI dashboard, the end user and their needs can vary:

1. The Analyst: The analyst prefers to see details and understand exactly what’s happening at a granular level. They prefer visuals like tables or combo charts that provide granular detail to support root cause analysis.
2. The Manager: The manager wants summarized data with clear, actionable insights to help operate the business. They prefer common charts & graphs and appreciate some detail, but only when it supports a specific insight.
3. The Executive: The executive needs high-level, crystal clear KPIs to track business health and topline performance. They prefer visuals like KPI cards or simple charts and require minimal detail, unless it adds critical context to KPIs. Each of these users has different needs and preferences when it comes to data visualization, and Power BI is versatile enough to cater to all of them.