What are the top tools for OLAP performance tuning and optimization?

1. Microsoft SQL Server Analysis Services (SSAS): ? Offers optimization features for multidimensional and tabular models. 2. Oracle OLAP: ? Provides tools for optimization, including materialized views and parallel processing. 3. IBM Cognos TM1: ? Supports performance tuning through cube design optimization and parallel processing. 4. SAP BW: ? Features aggregates, indexing, and partitioning for OLAP performance. 5. MicroStrategy: ? Optimizes cube design, caching, and query execution for enhanced performance. 6. Tableau: ? Utilizes data extracts, aggregations, and cache management for optimization. 7. Mondrian: ? An open-source OLAP server with optimization features like caching and aggregation tuning. 8. Pentaho Mondrian:

One thing I've found helpful, by default, the online application's second-level cache is set to off, all configuration entity lookups are always accessed from the database instead of the JVM’s cache. For integration managed server, a performance READ_ONLY setting is recommended. READ_ONLY is for performance as it loads the configuration entities into the second-level (JVM-level) cache and accesses them from the cache only. In READ_ONLY mode, the cached entities may not be updated on that JVM. If configuration entities change, a restart or cache flush must be issued to rebuild the cache.

Data modeling tools are indispensable when it comes to crafting an effective OLAP system that aligns with your business needs and facilitates rapid and efficient queries. These tools enable you to define both the logical and physical structure of your data, encompassing dimensions, hierarchies, measures, and relationships that underpin your OLAP cubes or tables. Moreover, data modeling tools facilitate the application of essential OLAP design principles, such as selecting the appropriate granularity level, minimizing unnecessary joins, and implementing indexes and partitions for performance optimization.

One time at work, a new Oracle batch job took 5-6 hours daily to run. After setting a specific degree of parallelism, rather than relying on Oracle's automatic assignment, we reduced its run time to just 2 minutes, greatly pleasing the stakeholders.

Data compression tools are invaluable for optimizing OLAP data, particularly when dealing with extensive datasets or constrained storage resources. These tools enable the application of diverse compression methods like dictionary encoding, run-length encoding, or bitmap encoding, tailored to your data’s type and distribution. Additionally, they assist in striking a balance between compression ratio, query performance, and CPU utilization, ensuring that your OLAP system operates efficiently. Notable examples of data compression tools include SQL Server Data Compression, Oracle Advanced Compression, and Vertica Compression, which offer effective solutions for managing data size and query speed.

Real-Time SQL Monitoring in Oracle databases significantly boosts OLAP systems. From my Oracle Utilities project experience, it was instrumental in swiftly pinpointing and resolving performance bottlenecks, optimizing resource allocation through detailed insights into SQL query resource usage, and enhancing query execution with comprehensive execution plans and statistics, leading to more efficient OLAP data processing.

Data visualization tools are software that help turn complex data sets into easy-to-understand visual representations like charts, graphs, and maps. These tools make it simpler to see patterns, trends, and outliers in large amounts of data. By visualizing the data, it becomes easier for people to understand and make decisions based on that information. Examples of these tools include Tableau, which is widely used for its interactive dashboards, Microsoft Power BI, known for integrating well with other Microsoft products, and Google Data Studio, which is great for creating customizable reports and is free to use.