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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.
PDF does not know of a word space character—the space between two letters and the space between two words differ only in quantity. Therefore, a title with ample letter-spacing for effect will usually end up with spaces in the word processor file, for example INTRODUCTION with spacing of 1 em as I N T R O D U C T I O N on the word processor.
BPEL—Business Process Execution Language; BPL—Broadband over Power Lines; BPM—Business Process Management; BPM—Business Process Modeling; bps—bits per second; BRM—Business Reference Model; BRMS—Business Rule Management System; BRR—Business Readiness Rating; BRS—Broadband Radio Service; BSA—Business Software Alliance; BSB ...
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.
Power Query is built on what was then [when?] a new query language called M.It is a mashup language (hence the letter M) designed to create queries that mix together data. It is similar to the F# programming language, and according to Microsoft it is a "mostly pure, higher-order, dynamically typed, partially lazy, functional language."
A common data warehouse example involves sales as the measure, with customer and product as dimensions. In each sale a customer buys a product. The data can be sliced by removing all customers except for a group under study, and then diced by grouping by product. A dimensional data element is similar to a categorical variable in statistics.