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The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured, and unstructured data sources. ETL ...
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.
A data engineer is a type of software engineer who creates big data ETL pipelines to manage the flow of data through the organization. This makes it possible to take huge amounts of data and translate it into insights . [ 28 ]
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.
Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]
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 nature, with their contents being erased prior to running an ETL process or immediately following successful completion of an ETL process.
Pentaho for Big Data: EE, CE: PDI plug-in: N/A: Pentaho for Big Data is a data integration tool based on Pentaho Data Integration. [2] It allows executing ETL jobs in and out of big data environments such as Apache Hadoop or Hadoop distributions such as Amazon, Cloudera, EMC Greenplum, MapR, and Hortonworks. [3]
The Thor platform is a cluster whose purpose is to be a data refinery for processing massive volumes of raw data for applications such as data cleansing and hygiene, extract, transform, load (ETL), record linking and entity resolution, large-scale ad hoc analysis of data, and creation of key data and indexes to support high-performance ...