Ablinskas_gynimo_uеѕduotis.pbix - Vytautas_е

: High-level summaries of primary metrics (Total Sales, Total Customers, Profit Margin).

The filename suggests a Power BI project created for a final defense or assessment (Lithuanian: "gynimo užduotis" means "defense task"). Based on the naming convention and the profile of Vytautas Šablinskas , who is a Backend Engineer at Vinted, this file likely represents a data visualization or business intelligence assignment. Vytautas_Е ablinskas_Gynimo_UЕѕduotis.pbix

A professional write-up for a Power BI defense typically covers the following architectural and analytical stages: 1. Data Connection and ETL Process (Power Query) : High-level summaries of primary metrics (Total Sales,

: Calculation of Key Performance Indicators (KPIs) such as: Total Revenue : SUM(Sales[Amount]) A professional write-up for a Power BI defense

: Combining multiple tables to create a unified dataset. 2. Data Modeling A robust model is essential for accurate cross-filtering.

: Inclusion of Slicers (date ranges, categories) and Drill-throughs to allow the viewer to move from high-level data to granular details. 4. Insights and Conclusions

: High-level summaries of primary metrics (Total Sales, Total Customers, Profit Margin).

The filename suggests a Power BI project created for a final defense or assessment (Lithuanian: "gynimo užduotis" means "defense task"). Based on the naming convention and the profile of Vytautas Šablinskas , who is a Backend Engineer at Vinted, this file likely represents a data visualization or business intelligence assignment.

A professional write-up for a Power BI defense typically covers the following architectural and analytical stages: 1. Data Connection and ETL Process (Power Query)

: Calculation of Key Performance Indicators (KPIs) such as: Total Revenue : SUM(Sales[Amount])

: Combining multiple tables to create a unified dataset. 2. Data Modeling A robust model is essential for accurate cross-filtering.

: Inclusion of Slicers (date ranges, categories) and Drill-throughs to allow the viewer to move from high-level data to granular details. 4. Insights and Conclusions