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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.
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. With the alternative method extract, load and transform (ELT), the loading job is the middle step, and the transformed data is loaded in its original format for data transformation in the target ...
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.
Extract, transform, load (ETL), procedure for copying data from one or more sources, transforming the data at the source system, and copying into a destination system Information extraction , automated extraction of structured information from unstructured or semi-structured machine-readable data [ 1 ] , for example using natural language ...
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.
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.
In the future, tools based on semantic web languages such as RDF, the Web Ontology Language (OWL) and standardized metadata registry will make data mapping a more automatic process. This process will be accelerated if each application performed metadata publishing. Full automated data mapping is a very difficult problem (see semantic translation).
Apache NiFi is a software project from the Apache Software Foundation designed to automate the flow of data between software systems.Leveraging the concept of extract, transform, load (ETL), it is based on the "NiagaraFiles" software previously developed by the US National Security Agency (NSA), which is also the source of a part of its present name – NiFi.