The VertiPaq storage engine uses separate data structures for each column. Tradeoff could be mitigated by a Mixed model design, and this option is described in the Switch to Mixed mode technique. Deciding to summarize fact-type data always involves tradeoffs. It could achieve a possible 99% reduction in model size, but reporting at day level-or individual order level-is no longer possible. Consider, then, that an even more significant data reduction could be achieved by grouping by date at month level. Significant data reduction could be achieved by summarizing all sales metrics, grouping by date, customer, and product. There is a distinct trade-off, however, resulting in loss of detail.įor example, a source sales fact table stores one row per order line. This technique can be used to raise the grain of fact-type tables. ![]() Perhaps the most effective technique to reduce a model size is to load pre-summarized data. Also, bear in mind that retrospective changes to time filters will not break reports it will just result in less (or more) data history available in reports. It is helpful to understand that time-based Power Query filters can be parameterized, and even set to use relative time periods (relative to the refresh date, for example, the past five years). We suggest you don't automatically load all available history, unless it is a known reporting requirement. For further information, read the Deep Dive into Query Parameters and Power BI Templates blog entry.įiltering by time involves limiting the amount of data history loaded into fact-type tables (and limiting the date rows loaded into the model date tables). You can leverage the use of Power Query parameters and Power BI Template files to simplify management and publication. This design approach will result in many smaller models, and it can also eliminate the need to define row-level security (but will require granting specific dataset permissions in the Power BI service, and creating "duplicate" reports that connect to each dataset). For example, instead of loading sales facts for all sales regions, only load facts for a single region. Removing rows is referred to as horizontal filtering.įiltering by entity involves loading a subset of source data into the model. It can be achieved by loading filtered rowsets into model tables for two different reasons: to filter by entity or by time. Model tables should be loaded with as few rows as possible. Removing columns can break reports or the model structure. Your requirements may change over time, but bear in mind that it's easier to add columns later than it is to remove them later. We recommend that you design models with exactly the right number of columns based on the known reporting requirements. Removing columns is referred to as vertical filtering.
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