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-- The logic of PHP mktime is followed where m or d can be zero to mean-- the previous unit, and -1 is the one before that, etc.-- Positive values carry forward. local date if not (1 <= m and m <= 12) then date = Date (y, 1, 1) if not date then return end date = date + ((m-1).. 'm') y, m = date. year, date. month end local days_hms if not ...
The PHP serialization format is the serialization format used by the PHP programming language. The format can serialize PHP's primitive and compound types, and also properly serializes references. [1] The format was first introduced in PHP 4. [2] In addition to PHP, the format is also used by some third-party applications that are often ...
In the data transformation stage, a series of rules or functions are applied to the extracted data in order to prepare it for loading into the end target. An important function of transformation is data cleansing, which aims to pass only "proper" data to the target. The challenge when different systems interact is in the relevant systems ...
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.
Oracle Data Integrator (ODI) is an extract, load, transform (ELT) tool (in contrast with the ETL common approach) produced by Oracle that offers a graphical environment to build, manage and maintain data integration processes in business intelligence systems.
Dates would be referenced in a fact table as foreign keys to a date dimension. The date dimension primary key could be a surrogate key or a number using the format YYYYMMDD. The date dimension can include other attributes like the week of the year, or flags representing work days, holidays, etc.
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 order to calculate the average and standard deviation from aggregate data, it is necessary to have available for each group: the total of values (Σx i = SUM(x)), the number of values (N=COUNT(x)) and the total of squares of the values (Σx i 2 =SUM(x 2)) of each groups.