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  2. Wide and narrow data - Wikipedia

    en.wikipedia.org/wiki/Wide_and_narrow_data

    Many statistical and data processing systems have functions to convert between these two presentations, for instance the R programming language has several packages such as the tidyr package. The pandas package in Python implements this operation as "melt" function which converts a wide table to a narrow one.

  3. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product. Each dimension table has a primary key on its Id column, relating to one of the columns (viewed as rows in the example schema) of the Fact_Sales table's three-column (compound) primary key (Date_Id, Store_Id, Product_Id).

  4. Help:Advanced table formatting - Wikipedia

    en.wikipedia.org/wiki/Help:Advanced_table_formatting

    For each table, insert an alpha-prefix on each column (making each row-token "|-" to sort as column zero, like prefix "Row124col00"), then sort into a new file, and then de-prefix the column entries. Again, bear in mind, the tedious hand-editing of items in each row is often faster than the potential delay of automated edits gone awry.

  5. Table (database) - Wikipedia

    en.wikipedia.org/wiki/Table_(database)

    In a database, a table is a collection of related data organized in table format; consisting of columns and rows.. In relational databases, and flat file databases, a table is a set of data elements (values) using a model of vertical columns (identifiable by name) and horizontal rows, the cell being the unit where a row and column intersect. [1]

  6. Dimension (data warehouse) - Wikipedia

    en.wikipedia.org/wiki/Dimension_(data_warehouse)

    Type 3 (Add new attribute): A new column is created for a new value. History is limited to the number of columns designated for storing historical data. Type 4 (Add history table): One table keeps the current value, while the history is saved in a second table. Type 5 (Combined Approach 1 + 4): Combination of type 1 and type 4. History is ...

  7. Wide-column store - Wikipedia

    en.wikipedia.org/wiki/Wide-column_store

    A wide-column store (or extensible record store) is a type of NoSQL database. [1] It uses tables, rows, and columns, but unlike a relational database, the names and format of the columns can vary from row to row in the same table. A wide-column store can be interpreted as a two-dimensional key–value store. [1]

  8. List of column-oriented DBMSes - Wikipedia

    en.wikipedia.org/wiki/List_of_column-oriented_DBMSes

    Released in 2016 to analyze data that is updated in real time CrateDB: Java C-Store: C++ The last release of the original code was in 2006; Vertica a commercial fork, lives on. DuckDB: C++ An embeddable, in-process, column-oriented SQL OLAP RDBMS Databend Rust An elastic and reliable Serverless Data Warehouse InfluxDB: Rust Time series database

  9. Help:Table - Wikipedia

    en.wikipedia.org/wiki/Help:Table

    style=max-width:Xem can be used in table headers. The following table excerpt is adapted from this version of List of countries by wealth per adult. The goal is to narrow the data columns, and have the country names spread out on one line each. All of this makes it easier when scanning down a country list.