starschemar: Obtaining Star Schemas from Flat Tables

In josesamos/starschemar: Obtaining Stars from Flat Tables

Obtaining Star Schemas from Flat Tables

Description

Transformations that allow obtaining star schemas from flat tables.

Details

From flat tables star schemas can be defined that can form constellations (star schema and constellation definition functions). Dimensions contain data without duplicates, operations to do data cleaning can be applied on them (data cleaning functions). Dimensions can be enriched by adding additional columns, sometimes using functions, others explicitly defined by the user (dimension enrichment functions). When new data is obtained, it is necessary to refresh the existing data with them by means of incremental refresh operations or delete data that is no longer necessary (incremental refresh functions). Finally, the results obtained can be exported to be consulted with other tools (results export functions) or through the defined query functions (query functions).

Star schema and constellation definition

Starting from a flat table, a dimensional model is defined specifying the attributes that make up each of the dimensions and the measurements in the facts. The result is a dimensional_model object. It is carried out through the following dimensional model definition functions:

• dimensional_model()

• define_dimension()

• define_fact()

A star schema is defined from a flat table and a dimensional model definition. Once defined, a star schema can be transformed by defining role playing dimensions, changing the writing style of element names or the type of dimension attributes. These operations are carried out through the following star schema definition and transformation functions:

• star_schema()

• role_playing_dimension()

• snake_case()

• character_dimensions()

Once a star schema is defined, we can rename its elements. It is necessary to be able to rename attributes of dimensions and measures of facts because the definition operations only allowed us to select columns of a flat table. For completeness also dimensions and facts can be renamed. To carry out these operations, the following star schema rename functions are available:

• rename_dimension()

• get_dimension_attribute_names()

• rename_dimension_attributes()

• rename_fact()

• get_measure_names()

• rename_measures()

Based on various star schemas, a constellation can be defined in which star schemas share common dimensions. Dimensions with the same name must be shared. It is defined by the following constellation definition function:

• constellation()

Data cleaning

Once the star schemas and constellations are defined, data cleaning operations can be carried out on dimensions. There are three groups of functions: one to obtain dimensions of star schemas and constellations; another to define data cleaning operations over dimensions; and one more to apply operations to star schemas or constellations.

Obtaining dimensions:

• get_dimension_names()

• get_dimension()

• get_conformed_dimension_names()

• get_conformed_dimension()

Update definition functions:

• record_update_set()

• match_records()

• update_record()

• update_selection()

• update_selection_general()

Modification application functions:

• modify_dimension_records()

• modify_conformed_dimension_records()

Dimension enrichment

To enrich a dimension with new attributes related to others already included in it, first, we export the attributes on which the new ones depend, then we define the new attributes, and import the table with all the attributes to be added to the dimension.

• enrich_dimension_export()

• enrich_dimension_import()

Incremental refresh

When new data is obtained, an incremental refresh of the data can be carried out, both of the dimensions and of the facts. Incremental refresh can be applied to both star schema and constellation, using the following functions:

• incremental_refresh_star_schema()

• incremental_refresh_constellation()

Sometimes the data refresh consists of eliminating data that is no longer necessary, generally because it corresponds to a period that has stopped being analysed but it can also be for other reasons. This data can be selected using the following function:

• filter_fact_rows()

Once the fact data is removed (using the other incremental refresh functions), we can remove the data for the dimensions that are no longer needed using the following functions:

• purge_dimensions_star_schema()

• purge_dimensions_constellation()

Results export

Once the data has been properly structured and transformed, it can be exported to be consulted with other tools or with R. Various export formats have been defined, both for star schemas and for constellations, using the following functions:

• star_schema_as_flat_table()

• star_schema_as_multistar()

• star_schema_as_tibble_list()

• constellation_as_multistar()

• constellation_as_tibble_list()

• multistar_as_flat_table()

Query functions

There are many multidimensional query tools available. The exported data, once stored in files, can be used directly from them. Using the following functions, you can also perform basic queries from R on data in the multistar format:

• dimensional_query()

• select_fact()

• select_dimension()

• filter_dimension()

• run_query()

josesamos/starschemar documentation built on May 6, 2024, 5:57 a.m.