What are the features of a power pivot?

Data Integration:

• Multiple Data Sources: Combine data from various sources like SQL Server, Access, Oracle, and more.
• Data Model: Create a data model by importing tables and establishing relationships between them.

Large Data Handling:

• In-Memory Analytics Engine: Handle large volumes of data efficiently using the in-memory analytics engine.
• Compression: Compresses data for faster processing and reduced memory usage.

Advanced Calculations:

• Data Analysis Expressions (DAX): Use DAX formulas to perform complex calculations and create custom measures.
• Calculated Columns: Add columns with custom calculations within the data model.

Relationships and Hierarchies:

• Table Relationships: Define relationships between tables to create a cohesive data model.
• Hierarchies: Create hierarchies to drill down into data easily (e.g., Year > Quarter > Month > Day).

Data Refresh:

• Scheduled Refresh: Set up scheduled data refreshes to keep your data up-to-date.
• Manual Refresh: Manually refresh data connections as needed.

PivotTables and PivotCharts:

• Enhanced PivotTables: Create more powerful and flexible PivotTables using the data model.
• PivotCharts: Visualize data using PivotCharts that update automatically with changes in the PivotTable.

Key Performance Indicators (KPIs):

• KPIs: Define and monitor KPIs to track performance against goals.

Role-Based Security:

• Row-Level Security: Implement row-level security to restrict data access based on user roles.

Slicer and Timeline Filters:

• Slicers: Use slicers to filter data interactively.
• Timelines: Filter data by date periods with timeline controls.

Integration with Power BI:

• Power BI Integration: Export data models to Power BI for further analysis and visualization.