The Power BI architecture can be divided into three stages:
1. Data Preparation Stage: This stage involves connecting to various data sources, extracting data, and preparing it for analysis. Users use Power Query, a powerful ETL tool within Power BI, to shape, clean, and transform the data. Power Query allows users to filter data, perform calculations, merge multiple data sources, and create relationships between tables. This stage is crucial to ensure that the data is in a suitable format for further analysis.
2. Data Modeling Stage: In this stage, users create a data model using Power Pivot, which is a data modeling engine within Power BI. The data model enables users to define relationships between data tables, create hierarchies, and add calculated measures. Power Pivot also provides a powerful in-memory engine to handle large datasets and perform complex calculations. This stage focuses on creating an efficient and meaningful structure for the data to support analysis and visualization.
3. Data Visualization Stage: The final stage involves creating interactive and visually appealing reports and dashboards using Power BI Desktop. Users design the layout, select suitable visualizations such as charts, tables, maps, and gauges, and customize them to present data insights effectively. Power BI Desktop offers a wide range of formatting and interactive capabilities, allowing users to slice and filter data, drill down into details, and create interactive reports. Reports and dashboards can then be published to the Power BI Service for sharing and collaboration.
These three stages, namely data preparation, data modeling, and data visualization, form the foundation of the Power BI architecture and workflow. Each stage plays a crucial role in transforming raw data into meaningful insights that can drive informed decision-making.
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