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For instance, the new application might use another database vendor and most likely a very different database schema. ETL can be used to transform the data into a format suitable for the new application to use. An example would be an expense and cost recovery system such as used by accountants, consultants, and law firms.
In the data warehouse practice of extract, transform, load (ETL), an early fact or early-arriving fact, [1] also known as late-arriving dimension or late-arriving data, [2] denotes the detection of a dimensional natural key during fact table source loading, prior to the assignment of a corresponding primary key or surrogate key in the dimension table.
For example, if the source system lists FirstName but the destination lists PersonGivenName, the mappings will still be made if these data elements are listed as synonyms in the metadata registry. Semantic mapping is only able to discover exact matches between columns of data and will not discover any transformation logic or exceptions between ...
Spatial extract, transform, load (spatial ETL), also known as geospatial transformation and load (GTL), is a process for managing and manipulating geospatial data, for example map data. It is a type of extract, transform, load (ETL) process, with software tools and libraries specialised for geographical information.
For example, an automotive manufacturer might maintain a modem-pool that all of its hundreds of suppliers are required to dial into to perform EDI. However, if a supplier does business with several manufacturers, it may need to acquire a different modem (or VPN device, etc.) and different software for each one.
An operational data store (ODS) is used for operational reporting and as a source of data for the enterprise data warehouse (EDW). It is a complementary element to an EDW in a decision support environment, and is used for operational reporting, controls, and decision making, as opposed to the EDW, which is used for tactical and strategic decision support.
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 warehouse. Dbt has the goal of allowing analysts to work more like software engineers, in line with the dbt viewpoint.
However, ETL is used to merge and structure data into a target database, [6] often a data warehouse. Data blending differs slightly as it's about joining data for a specific use case at a specific time. [7] With some software, data isn't written into a database, which is very different to ETL. For example, with Google Data Studio. [8]
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