Birst provides best of breed capabilities for multidimensional analysis. Quickly slice and dice your data from a number of different perspectives as well as identify complex data relationships through the use of powerful cross-dimensional calculations.
Birst requires no cubes to be built and maintained, offloading IT from the resource-intensive and time-consuming task of constantly having to maintain and optimize a growing cube farm. With Birst, IT simply defines a logical model and Birst takes care of generating an optimized physical instantiation which gives analysts true ad hoc access to the entire data warehouse. Analysts can quickly issue their own ad hoc queries and use drill, filter, and pivot functionality to analyze result sets, all from a single easy-to-use interface.
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Unlike other OLAP products, Birst does not restrict dimensional access to the data. Birst constructs a dynamic logical cube of all data that it is mapped to. It provides the full richness, scope and depth of information that can be possible analyzed. As long as dimensional relationships between various data elements exist, users can analyze the data that way. Birst’s ROLAP engine provides support for advanced concepts like positional references, cell-based calculations and differentiation between slicers and filters. Using powerful positional calculations, Birst enables users to analyze how a value in a given cell of the cube relates to values elsewhere. Birst’s best-in-class calculation capabilities include dimension-specific aggregation, inheritance, business rules, multi-pass calculations and virtual measures.
Birst unique caching layer provides an improvement over traditional OLAP caching solutions. In addition to exact and fuzzy cache matching, Birst generates dynamic cubes to maximize query re-use and minimize database load. These dynamic cubes are indexed structures that provide far better re-use and generate lower database load than traditional caching approaches. They are dynamically partitioned amongst servers to minimize IO contention and to allow better memory caching, ultimately resulting in a far more scalable solution.
Birst’s ROLAP engine works on operational tables just as well as on star schemas. Birst can map onto “opaque views” – essentially inline views with multi-pass operations. When combining data at different levels of aggregation, Birst will push down multiple sub queries and via multi-pass pull the individual results together into a single result set.
