The VertiPaq engine, now known as the xVelocity engine, is a high-performance in-memory data storage and query processing engine developed by Microsoft. It is a fundamental component of Microsoft’s analytical data processing technologies, primarily used in products like SQL Server Analysis Services (SSAS), Power BI, and Power Pivot for Excel.

The main purpose of the VertiPaq engine is to enable rapid data analysis on large datasets by leveraging in-memory columnar storage and compression techniques. Instead of the traditional row-based storage, where each record is stored in a separate row, the VertiPaq engine stores data in a columnar format. This format allows for better data compression and more efficient data retrieval during analytical queries.

Key features of the VertiPaq engine (xVelocity):

1. In-Memory Storage: Data is loaded into memory, reducing the need to read from disk during query execution, leading to faster data access and query performance.

2. Columnar Storage: Data is stored in columns rather than rows, which improves data compression and minimizes the number of I/O operations required during queries.

3. Compression: The VertiPaq engine employs advanced compression algorithms that reduce memory usage while maintaining query performance. This is particularly beneficial for datasets with large volumes of repeating values.

4. Parallel Processing: The engine can take advantage of multiple cores and parallelism to speed up query processing.

5. Aggregation and Query Optimizations: The VertiPaq engine employs various techniques to optimize query execution, such as aggregating data in memory to minimize data scanning and efficiently using indexes.

6. DirectQuery: In addition to in-memory mode, Power BI and SSAS also support a DirectQuery mode where data stays in the source system, and only aggregated or filtered results are fetched for visualization and analysis.

By using the VertiPaq engine, Microsoft’s analytical products can deliver real-time or near-real-time data analysis capabilities, providing a responsive and interactive experience for end-users even with large and complex datasets. This technology has significantly improved the performance and capabilities of Microsoft’s BI (Business Intelligence) and data analytics offerings.