![]() ![]() 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. Model structure, by supporting model relationships, model calculations, security roles, and even data color formattingĬolumns that don't serve these purposes can probably be removed.Reporting, to achieve report designs that appropriate filter, group, and summarize model data.Model table columns serve two main purposes: There are eight different data reduction techniques covered in this article. Smaller table row counts can result in faster calculation evaluations, which can deliver better overall query performance.Smaller models achieve faster data refresh, resulting in lower latency reporting, higher dataset refresh throughput, and less pressure on source system and capacity resources.It allows more models to be concurrently loaded for longer periods of time, resulting in lower eviction rates. Smaller model sizes reduce contention for capacity resources, in particular memory.For further information, read the Power BI Premium support for large datasets article. Shared capacity can host models up to 1 GB in size, while Premium capacities can host larger models depending on the SKU. Larger model sizes may not be supported by your capacity.It is especially true for large models, or models that you anticipate will grow to become large over time. Further, when persisted to disk an additional 20% reduction can be achieved.ĭespite the efficiencies achieved by the VertiPaq storage engine, it is important that you strive to minimize the data that is to be loaded into your models. When source data is loaded into memory, it is possible to see 10x compression, and so it is reasonable to expect that 10 GB of source data can compress to about 1 GB in size. Import models are loaded with data that is compressed and optimized and then stored to disk by the VertiPaq storage engine. It describes different techniques to help reduce the data loaded into Import models. ![]() This article targets Power BI Desktop data modelers developing Import models. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |