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Extract, load, transform (ELT) is an alternative to extract, transform, load (ETL) used with data lake implementations. In contrast to ETL, in ELT models the data is not transformed on entry to the data lake, but stored in its original raw format.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
Data loading, or simply loading, is a part of data processing where data is moved between two systems so that it ends up in a staging area on the target system. With the traditional extract, transform and load (ETL) method, the load job is the last step, and the data that is loaded has already been transformed.
In computing and data management, data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks, including: [1] Data transformation or data mediation between a data source and a destination
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. [1] A common use of spatial ...
Figure 1: Simple schematic for a data warehouse. The Extract, transform, load (ETL) process extracts information from the source databases, transforms it and then loads it into the data warehouse. Figure 2: Simple schematic for a data-integration solution. A system designer constructs a mediated schema against which users can run queries.
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.
A staging area, or landing zone, is an intermediate storage area used for data processing during the extract, transform and load (ETL) process. The data staging area sits between the data source(s) and the data target(s), which are often data warehouses, data marts, or other data repositories. [1] Data staging areas are often transient in ...