Python is a versatile interpreted programming language*. It is characterized by a comparatively clear syntax, high modularity, a broad selection of standard libraries and an active community that provides numerous additional modules.
dbt (Data Build Tool) is an open-source framework focused on transforming data within the analytics engineering pipeline. Unlike traditional ETL tools that extract, transform and load data, dbt handles only the transformation step directly inside the data warehouse or data lake. The key advantage is that dbt provides SQL-based transformations in a structured, version-controlled and testable format. This allows teams to develop data models like software – with modularity, reusability and automated testing.
Data Vault 2.0 describes a design paradigm for data delivery. A key advantage of Data Vault 2.0 is its ability to respond very flexibly to changes in business processes without fundamentally modifying underlying structures. To achieve this, Data Vault 2.0, in contrast to a traditional data warehouse, is not the defined, aggregated truth of all data but rather the neutral provision of all available data. Data aggregation takes place on top of it, for example in data marts.