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  2. Data build tool - Wikipedia

    en.wikipedia.org/wiki/Data_build_tool

    Dbt enables analytics engineers to transform data in their warehouses by writing select statements, and turns these select statements into tables and views. Dbt does the transformation (T) in extract, load, transform (ELT) processes – it does not extract or load data, but is designed to be performant at transforming data already inside of a ...

  3. Apache Hive - Wikipedia

    en.wikipedia.org/wiki/Apache_Hive

    Apache Hive is a data warehouse software project. It is built on top of Apache Hadoop for providing data query and analysis. [3] [4] Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.

  4. Enterprise bus matrix - Wikipedia

    en.wikipedia.org/wiki/Enterprise_bus_matrix

    The bus matrix purpose is one of high abstraction and visionary planning on the data warehouse architectural level. By dictating coherency in the development and implementation of an overall data warehouse the bus architecture approach enables an overall vision of the broader enterprise integration and consistency while at the same time dividing the problem into more manageable parts [2 ...

  5. ERPNext - Wikipedia

    en.wikipedia.org/wiki/ERPNext

    ERPNext is a free and open-source integrated Enterprise resource planning (ERP) software developed by an Indian software company Frappe Technologies Pvt. Ltd. [2] [3] It is built on the MariaDB database system using Frappe, a Python based server-side framework.

  6. Apache Iceberg - Wikipedia

    en.wikipedia.org/wiki/Apache_Iceberg

    Apache Iceberg is a high performance open-source format for large analytic tables.Iceberg enables the use of SQL tables for big data while making it possible for engines like Spark, Trino, Flink, Presto, Hive, Impala, StarRocks, Doris, and Pig to safely work with the same tables, at the same time. [1]

  7. Dimensional modeling - Wikipedia

    en.wikipedia.org/wiki/Dimensional_modeling

    The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the data warehouse. The basics in the design build on the actual business process which the data warehouse should cover. Therefore, the first step in the model is to describe the business process which ...

  8. Anchor modeling - Wikipedia

    en.wikipedia.org/wiki/Anchor_Modeling

    The earliest installations using anchor modeling were made 2004 in Sweden when a data warehouse for an insurance company was built using the technique. In 2007 the technique was being used in a few data warehouses and one online transaction processing (OLTP) system, and it was presented internationally by Lars Rönnbäck at the 2007 ...

  9. Dimensional fact model - Wikipedia

    en.wikipedia.org/wiki/Dimensional_fact_model

    Data warehouses (DWs) are databases used by decision makers to analyze the status and the development of an organization. DWs are based on large amounts of data integrated from heterogeneous sources into multidimensional databases, and they are optimized for accessing data in a way that comes naturally to human analysts (e.g., OLAP applications).