A Date warehouse (DW or ) (English term translatable data warehouse) is a computer file containing the data of an organization.
The amount of data that companies store for analytical purposes is growing every year by about 50%. Only a few years ago, the data used for business intelligence were stored in a centralized data warehouse and data mart in a few departments. Now, the growing demand for business intelligence data of good quality has created a wide range of data repositories distributed within organizations, which has increased costs and complexity for companies looking to maximize the use of analytical data .
information systems that are based on a traditional database systems are often called OLTP (online transaction processing). Their function is to run the daily operations: data editing and simple read operations. A data-warehouse, however, is the heart of an OLAP system (onlineanalytical processing). Their function is to provide support to operations, data analysis and decision making.
For the achievement of DW is juxtaposed with two solutions:
- The first is the 'use of relational technology, data is stored using tables, but the analysis operations are carried out efficiently using special data structures. Systems of this type are said ROLAP (Relational OLAP ).
- the latter, more radical, stores data directly in the form of multi-dimensional structures using vector data. Systems of this type are said MOLAP (Multidimensional OLAP ).
Creating an OLAP database is, more generally, carrying a photograph of information at a given moment and turn these items of information in multidimensional data. E 'possible to perform further queries on the same data to get answers in time much lower than similar operations on other types of databases. The OLAP
Specimen layout created for this purpose is called a multidimensional cube . It is a powerful entity to perform all possible combinations in a table extracted for analysis. The multi-dimensional cube can be created in several ways but the best known is the one that uses the pattern "star". At the center of the diagram is the table of "facts" that lists the main elements which will be built on the question. Connect to this table there are various tables of the "dimensions" that specify how the data will be aggregated. The calculation of the possible combinations of these groupings form an OLAP structure that potentially could contain all the answers for each combination. The
basic functions of an OLAP tool are: Drill down
that adds a dimension of analysis by disaggregating the data. For example, an analyst may wish to add the distribution of the amount sold in the sales areas, carrying out the 'drill down on transaction Area. attribute values \u200b\u200btake zone 'North', 'center', 'South'.
Roll up eliminates a dimension of analysis, re-aggregated data. For example, an analyst might lose interest in the monthly breakdown of sales, being more interested in highlighting areas for the subdivision through the operation roll up on months.
Alternating
operations roll up and drill down, the analyst can highlight the dependency of the phenomena represented in the facts on social dimensions, and thus highlight their properties. Note that the roll-up operation can be carried out by using the query results, while the drill down operation usually requires a reformulation of the question, because it can add columns not present in it.
Other functions include:
Slice and Dice , functionality that lets you restrict the analysis only to certain parts of the scale and only some of the dimensions proposed.
Filtering you need to select a portion dei dati in modo da dare una risposta utile all'utente in modo diretto o indiretto, migliorando la funzione di ricerca.
The amount of data that companies store for analytical purposes is growing every year by about 50%. Only a few years ago, the data used for business intelligence were stored in a centralized data warehouse and data mart in a few departments. Now, the growing demand for business intelligence data of good quality has created a wide range of data repositories distributed within organizations, which has increased costs and complexity for companies looking to maximize the use of analytical data .
information systems that are based on a traditional database systems are often called OLTP (online transaction processing). Their function is to run the daily operations: data editing and simple read operations. A data-warehouse, however, is the heart of an OLAP system (onlineanalytical processing). Their function is to provide support to operations, data analysis and decision making.
For the achievement of DW is juxtaposed with two solutions:
- The first is the 'use of relational technology, data is stored using tables, but the analysis operations are carried out efficiently using special data structures. Systems of this type are said ROLAP (Relational OLAP ).
- the latter, more radical, stores data directly in the form of multi-dimensional structures using vector data. Systems of this type are said MOLAP (Multidimensional OLAP ).
Creating an OLAP database is, more generally, carrying a photograph of information at a given moment and turn these items of information in multidimensional data. E 'possible to perform further queries on the same data to get answers in time much lower than similar operations on other types of databases. The OLAP
Specimen layout created for this purpose is called a multidimensional cube . It is a powerful entity to perform all possible combinations in a table extracted for analysis. The multi-dimensional cube can be created in several ways but the best known is the one that uses the pattern "star". At the center of the diagram is the table of "facts" that lists the main elements which will be built on the question. Connect to this table there are various tables of the "dimensions" that specify how the data will be aggregated. The calculation of the possible combinations of these groupings form an OLAP structure that potentially could contain all the answers for each combination. The
basic functions of an OLAP tool are: Drill down
that adds a dimension of analysis by disaggregating the data. For example, an analyst may wish to add the distribution of the amount sold in the sales areas, carrying out the 'drill down on transaction Area. attribute values \u200b\u200btake zone 'North', 'center', 'South'.
Roll up eliminates a dimension of analysis, re-aggregated data. For example, an analyst might lose interest in the monthly breakdown of sales, being more interested in highlighting areas for the subdivision through the operation roll up on months.
Alternating
operations roll up and drill down, the analyst can highlight the dependency of the phenomena represented in the facts on social dimensions, and thus highlight their properties. Note that the roll-up operation can be carried out by using the query results, while the drill down operation usually requires a reformulation of the question, because it can add columns not present in it.
Other functions include:
Slice and Dice , functionality that lets you restrict the analysis only to certain parts of the scale and only some of the dimensions proposed.
Filtering you need to select a portion dei dati in modo da dare una risposta utile all'utente in modo diretto o indiretto, migliorando la funzione di ricerca.
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