Excel (GUI, formulas, VBA) handles up to 1 million rows. However, with the rise of big data, many companies need to process far larger datasets, making traditional methods inadequate. To address this, Microsoft launched Power BI, hailed as one of its greatest inventions in a decade, capable of handling millions or even tens of millions of rows.
Power BI has two usage modes: embedded within Excel or standalone via Power BI Desktop. It consists of two core components:
Power Query: for data cleaning.
Power Pivot: for data modeling and analysis.
Processing Data via Power Query
Newer Excel versions integrate partial Power Query functionality, allowing large dataset imports and cleaning. Large-scale cleaning tasks are similar to small/medium-scale ones but involve bigger data volumes.
Power Query works like Excel: users can operate via GUI (mouse/keyboard) or script programming using M Language. While designed for large data, it also benefits small/medium-scale tasks. Note: Power Query does not support the Excel object model and cannot directly interact with Excel worksheets.
Processing Data via Power Pivot
"Pivot" refers to Pivot Tables, so Power Pivot means "super pivot tables"—an upgrade to Excel’s native Pivot Tables. It is available as an Excel add-in.
Power Pivot integrates multi-source data for modeling and analysis, far exceeding Excel’s Pivot Tables. It uses DAX Language, comparable to Excel Pivot Table formulas.
Processing Data via Power BI Desktop
Power BI Desktop (downloadable from Microsoft’s website) integrates Power Query and Power Pivot. Its interface (Figure 1-1) includes tabs like File, Home, Insert, Modeling, and View.
The usage of Power Query and Power Pivot in Power BI Desktop mirrors their Excel counterparts, supporting M Language and DAX. Additionally, Power BI Desktop offers data visualization and script programming (Python/R).
Python scripts can replace manual operations for data import, transformation, and visualization. R scripts are also supported.