There are several advanced techniques for managing and optimizing SQL Server Analysis Services (SSAS) tabular and multidimensional models. In this answer, some of the important techniques are discussed.
1. Partitioning: Partitioning is a technique that can significantly improve query performance and reduce processing time. Partitioning divides a table into smaller, more manageable pieces called partitions. Each partition can be processed and queried independently, which enables the database engine to process queries in parallel. Partitioning is particularly useful for large tables or tables that store historical data.
2. Aggregations: Aggregations are pre-calculated summaries of data that can be used to speed up queries. In SSAS, aggregations can be created at different levels of granularity, allowing users to query the data quickly without the need to calculate aggregates on the fly. In addition, aggregations can be stored in memory, further improving query performance.
3. Measures and Hierarchies: Measures are the numeric data that are used for analysis in SSAS. Hierarchies are the logical grouping of data in the cube. Optimizing measures and hierarchies can significantly improve query response times. Creating hierarchies can speed up queries by creating rollup calculations on the fly. Measures can be optimized by using different aggregation functions such as sum, count, average, and distinct count.
4. Query Optimization: Query optimization can improve query performance by selecting the most efficient query execution plan. For example, using indexes, avoiding predicates that cannot be indexed, and optimizing joins can significantly improve query performance. Query optimization can also be achieved by caching frequently used queries, using aggregations and aggregating tables, and using row-level security to restrict access to data.
5. Data Compression: Data compression can reduce the size of the data stored in SSAS, which can improve query performance and reduce memory usage. In SSAS, data compression can be achieved through a technique called column-based compression. Column-based compression reduces the size of data by storing repeated values only once.
6. Processing Modes: There are three processing modes in SSAS: Full, Incremental, and Process Add. Full mode processes the entire database, while Incremental mode processes only the changes since the last processing. Process Add mode processes only the new data added to the database. By using the appropriate processing mode, processing time can be reduced, and query performance can be improved.
In summary, by using techniques such as partitioning, aggregations, measures and hierarchies optimization, query optimization, data compression, and processing modes, SSAS models can be managed and optimized to deliver the best performance.