Примери за използване на Data warehouses на Английски и техните преводи на Български
{-}
-
Colloquial
-
Official
-
Medicine
-
Ecclesiastic
-
Ecclesiastic
-
Computer
Databases and data warehouses.
Data warehouses and management information systems.
In comparison, data warehouses require.
Traffic anomalies to, from or between data warehouses.
Data warehouses, data marts,data stores, data platform.
The basic difference between search engines and data warehouses is that search.
Most vendors would say that the data warehouses are difficult and expensive to do, and that they are not advisable.
The source data for OLAP is OLTP databases that are commonly stored in data warehouses.
Many vendors will tell you that data warehouses are hard to build, as well as expensive.
Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.
Many tables in corporate databases and data warehouses contain historical data accumulated over long periods of time.
Data warehouses may hold large amounts of information, sometimes in smaller logical units called Data marts.
Of advanced computer system architecture, operational systems,database systems and data warehouses, computer networks, security of computer systems;
In most data warehouses, information can be inserted by different parties culling data from many sources.
To distinguish between the concepts of business intelligence and data warehouses, Forrester Research defines business intelligence in one of two ways.
Data warehouses usually contain huge amounts of data, divided in logical units called dependent data marts.
EDW's became a byword for malformed and disjointed data warehouses that organisation struggled to control due to lack of strategy and vision.
Data warehouses often hold large amounts of information which are sometimes subdivided into smaller logical units called dependent data marts.
In order to distinguish between concepts of business intelligence and data warehouses, Forester Research often defines business intelligence in one of two ways.
However, all data warehouses are not used for business intelligence, nor do all BI applications need a data warehouse.
Cloud data warehouses like Amazon Redshift will continue to pull data, and cloud analytics will become more prevalent as a result.
Quality processing(especially when there is a large number of employees in various positions) requires the investment of resources, such as computer systems and software,reliable data warehouses, trained professionals, etc.
Cloud data warehouses will continue to be extremely popular data destinations and as a result, analytics will become more prevalent.
Organizations will be able to make their data work more quickly, productively, and securely,with the ability to gain insights from all data sources, data warehouses, and big data analytics systems.
However, not all data warehouses are used for business intelligence nor do all business intelligence applications require a data warehouse.
According to Gartner, the leaders quadrant“contains those vendors that demonstrate the greatest degree of support for data warehouses of all sizes, with large numbers of concurrent users and management of mixed data warehousing workloads.”.
Kim predicts that cloud data warehouses like Amazon Redshift will continue to pull data, and cloud analytics will become more prevalent as a result.
The Leaders quadrant, as described in the Gartner report,'contains the vendors that demonstrate the greatest support for data warehouses of all sizes, with large numbers of concurrent users and management of mixed data warehousing workloads.
Note: In data warehouses and multidimensional databases, large tables consisting of mostly numeric data are often referred to as“fact tables”.