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ETL tools in most cases contain a GUI that helps users conveniently transform data, using a visual data mapper, as opposed to writing large programs to parse files and modify data types. While ETL tools have traditionally been for developers and IT staff, research firm Gartner wrote that the new trend is to provide these capabilities to ...
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.
Extract, transform, load tools are software packages that facilitate the performing of ETL tasks. Pages in category "Extract, transform, load tools" The following 35 pages are in this category, out of 35 total.
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.
Support for multiple datasources (or multiple connections to a single database) in an ETL file. Support for many useful JDBC features, e.g. parameters in SQL including file blobs and JDBC escaping. Performance. Performance and low memory usage are one of the primary goals. Support for evaluated expressions and properties (JEXL syntax)
Dbt uses YAML files to declare properties. seed is a type of reference table used in dbt for static or infrequently changed data, like for example country codes or lookup tables ), which are CSV based and typically stored in a seeds folder .
IBM InfoSphere DataStage is an ETL tool and part of the IBM Information Platforms Solutions suite and IBM InfoSphere. It uses a graphical notation to construct data integration solutions and is available in various versions such as the Server Edition, the Enterprise Edition, and the MVS Edition.
Typical unstructured data sources include web pages, emails, documents, PDFs, social media, scanned text, mainframe reports, spool files, multimedia files, etc. Extracting data from these unstructured sources has grown into a considerable technical challenge, where as historically data extraction has had to deal with changes in physical hardware formats, the majority of current data extraction ...