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A SCHEMA is COLLECTION of DATABASE OBJECTS of a USER.

A schema is collection of database objects of a User.

A rule defined on a COLUMN (or set of columns) in ONE table that allows the insert or UPDATE of a ROW only if the value for the column or set of columns (the dependent value) matches a value in a column of a related table (the referenced value). It also specifies the type of data manipulation allowed on referenced data and the action to be performed on dependent data as a RESULT of any action on referenced data.

A rule defined on a column (or set of columns) in one table that allows the insert or update of a row only if the value for the column or set of columns (the dependent value) matches a value in a column of a related table (the referenced value). It also specifies the type of data manipulation allowed on referenced data and the action to be performed on dependent data as a result of any action on referenced data.

A ROW is stored in a hash cluster based on the result of applying a hash FUNCTION to the row's cluster KEY value. All ROWS with the same hash key value are stores TOGETHER on disk.

A row is stored in a hash cluster based on the result of applying a hash function to the row's cluster key value. All rows with the same hash key value are stores together on disk.

A PRIVATE SYNONYMS can be ACCESSED only by the OWNER.

A Private Synonyms can be accessed only by the owner.

A database LINK is a named object that describes a "PATH" from ONE database to ANOTHER.

A database link is a named object that describes a "path" from one database to another.

A DATABASE is DIVIDED into Logical STORAGE Unit called table spaces. A table space is used to GROUPED related logical structures TOGETHER.

A database is divided into Logical Storage Unit called table spaces. A table space is used to grouped related logical structures together.

A Database contains one or more Rollback SEGMENTS to TEMPORARILY store "UNDO" INFORMATION.

A Database contains one or more Rollback Segments to temporarily store "undo" information.

A data FILE can be associated with only ONE database. Once created a data file can't CHANGE size. One or more data files form a logical UNIT of database storage CALLED a table space.

A data file can be associated with only one database. Once created a data file can't change size. One or more data files form a logical unit of database storage called a table space.

The basic advantage of RAID is to speed up the DATA READING from permanent storage DEVICE (hard disk).

The basic advantage of RAID is to speed up the data reading from permanent storage device (hard disk).

Mapping PARAMETER defines the CONSTANT value and it cannot CHANGE the value throughout the session. Mapping VARIABLES defines the value and it can be change throughout the session.

Mapping Parameter defines the constant value and it cannot change the value throughout the session. Mapping Variables defines the value and it can be change throughout the session.

Meta data is nothing but information about data. It contains the information (i.e. data) about the graphs, its related FILES, abinitio COMMANDS, server information ETC i.e. all KINDS of information about project related information etc.

Meta data is nothing but information about data. It contains the information (i.e. data) about the graphs, its related files, abinitio commands, server information etc i.e. all kinds of information about project related information etc.

MOLAP

MOLAP

DEPENDENT departments are those, which depend on a DATA ware to for their data.Independent department are those, which get their data DIRECTLY from the operational data SOURCES in the organization.

Dependent departments are those, which depend on a data ware to for their data.Independent department are those, which get their data directly from the operational data sources in the organization.

If U have one to MAY relation SHIP in the data then only we choose SNOWFLAKE schema, as per the performance-wise every-one go for the Star schema. Moreover, if the ETL is concerned with reporting means choose for snowflake because this schema provides more BROWSING capability than the former schema.

If u have one to may relation ship in the data then only we choose snowflake schema, as per the performance-wise every-one go for the Star schema. Moreover, if the ETL is concerned with reporting means choose for snowflake because this schema provides more browsing capability than the former schema.

Let us take one ex: Suppose 'XYZ' is customer in Bangalore, he was residing in the city from the last 5 years, in the period of 5 years he has made purchases WORTH of 3 lacs. Now, he MOVED to 'HYD'. When you update the 'XYZ' city to 'HYD' in your Warehouse, all the purchases by him will show in city 'HYD' only. This MAKES warehouse inconsistent. Here CITY is the CRITICAL Column. Solution is use Surrogate Key.

Let us take one ex: Suppose 'XYZ' is customer in Bangalore, he was residing in the city from the last 5 years, in the period of 5 years he has made purchases worth of 3 lacs. Now, he moved to 'HYD'. When you update the 'XYZ' city to 'HYD' in your Warehouse, all the purchases by him will show in city 'HYD' only. This makes warehouse inconsistent. Here CITY is the Critical Column. Solution is use Surrogate Key.

Many-many RELATIONS can be IMPLEMENTED by using snowflake SCHEMA .With a max of N dimensions.

Many-many relations can be implemented by using snowflake schema .With a max of n dimensions.

A multi-dimensional structure called the data cube. A data abstraction allows one to VIEW aggregated data from a NUMBER of perspectives. CONCEPTUALLY, the cube consists of a core or base cuboids, surrounded by a collection of sub-cubes/cuboids that represent the aggregation of the base cuboids along one or more dimensions. We refer to the DIMENSION to be aggregated as the measure attribute, while the remaining dimensions are known as the feature attributes.

A multi-dimensional structure called the data cube. A data abstraction allows one to view aggregated data from a number of perspectives. Conceptually, the cube consists of a core or base cuboids, surrounded by a collection of sub-cubes/cuboids that represent the aggregation of the base cuboids along one or more dimensions. We refer to the dimension to be aggregated as the measure attribute, while the remaining dimensions are known as the feature attributes.

We cannot use SQL QUERIES in filter transformation. It will not allow you to override default sql query LIKE other transformations (Source Qualifier, lookup)

We cannot use sql queries in filter transformation. It will not allow you to override default sql query like other transformations (Source Qualifier, lookup)

After the completion of REPORTING, REPORTS will be sent to business analysts. They will ANALYZE the data from different points of view so that they can make a PROPER business DECISIONS.

After the completion of reporting, reports will be sent to business analysts. They will analyze the data from different points of view so that they can make a proper business decisions.

The GENERATED report will be SENT to the concerned business users through WEB or LAN.

The generated report will be sent to the concerned business users through web or LAN.

Method 1 is system DEVELOP lifecycle create by ARTHUR ANDERSON a while back.

Method 1 is system develop lifecycle create by Arthur Anderson a while back.

Data modeling is the process of designing a data BASE MODEL. In this data model data will be stored in TWO types of table fact table and dimension table.

Fact table contains the transaction data and dimension table contains the master data. Data mining is process of finding the hidden trends is CALLED the data mining.

Data modeling is the process of designing a data base model. In this data model data will be stored in two types of table fact table and dimension table.

Fact table contains the transaction data and dimension table contains the master data. Data mining is process of finding the hidden trends is called the data mining.

Data analysis: CONSIDER that you are running a business and u store the data of that; in some FORM SAY in register or in a comp and at the year end you want KNOW the profit or LOSS then it called data analysis .Data analysis use: then u want to know which product was sold the highest and if the business is running in a loss then finding, where we went wrong we do analysis.

Data analysis: consider that you are running a business and u store the data of that; in some form say in register or in a comp and at the year end you want know the profit or loss then it called data analysis .Data analysis use: then u want to know which product was sold the highest and if the business is running in a loss then finding, where we went wrong we do analysis.

Informatica Architecture CONTAINS Repository, Repository server, Repository server ADMINISTRATION console, SOURCES, repository server and DATA WAREHOUSING and it have the Designer, Work for manager, work for monitor combination of all these are called Informatica Architecture.

Informatica Architecture contains Repository, Repository server, Repository server administration console, sources, repository server and Data warehousing and it have the Designer, Work for manager, work for monitor combination of all these are called Informatica Architecture.

The DEVELOPER CREATED the mapping that can be tested INDEPENDENTLY by the developer INDIVIDUALLY.

The Developer created the mapping that can be tested independently by the developer individually.

We can LINK ONE UNIVERSE to other universe in Universe PARAMETERS.

We can link one universe to other universe in Universe parameters.

As the term suggests, a real-time data warehouse is a system, which reflects all changes to its sources in real time. As simple as it SOUNDS, this is still an area of active research in the field. In traditional DWH, the operational system(s) are KEPT separate from the DWH for a good reason.

The Operational systems are DESIGNED to accept inputs or changes to data regularly, HENCE have a good chance of being regularly queried. On the other hand, a DWH is SUPPOSED to do just the opposite - it is used to query data for reports only. No changes to data, through user actions is expected (or designed). The only inputs could come from the ETL feed at stipulated times. The ETL would source its data from the Operational systems just explained above.

To create a real-time DWH we would have to merge both systems (several ways are being explored), a concept that is against the reason of creating a DWH. Bigger challenges occur in terms of updating aggregated data in facts at real time, still maintaining the surrogate keys.

Besides, we would need lightening fast hardware to try this.Near Real time DWH is a trade-off between the conventional design and the dream of all clients today. The frequency of ETL updates in higher in this case for e.g. once in 2 hours. We can also analyze and use selective refreshes at shorter time intervals, while complete refreshes may still be kept further apart. Selective refreshes would look at only those tables that get updated regularly.

As the term suggests, a real-time data warehouse is a system, which reflects all changes to its sources in real time. As simple as it sounds, this is still an area of active research in the field. In traditional DWH, the operational system(s) are kept separate from the DWH for a good reason.

The Operational systems are designed to accept inputs or changes to data regularly, hence have a good chance of being regularly queried. On the other hand, a DWH is supposed to do just the opposite - it is used to query data for reports only. No changes to data, through user actions is expected (or designed). The only inputs could come from the ETL feed at stipulated times. The ETL would source its data from the Operational systems just explained above.

To create a real-time DWH we would have to merge both systems (several ways are being explored), a concept that is against the reason of creating a DWH. Bigger challenges occur in terms of updating aggregated data in facts at real time, still maintaining the surrogate keys.

Besides, we would need lightening fast hardware to try this.Near Real time DWH is a trade-off between the conventional design and the dream of all clients today. The frequency of ETL updates in higher in this case for e.g. once in 2 hours. We can also analyze and use selective refreshes at shorter time intervals, while complete refreshes may still be kept further apart. Selective refreshes would look at only those tables that get updated regularly.

If you want to run the GRAPH through GDE then after save the graph just press F5 BUTTON of your keyboard, it will run AUTOMATICALLY. If you want to run through the SHELL script then you have to fire the command at your UNIX box.

If you want to run the graph through GDE then after save the graph just press F5 button of your keyboard, it will run automatically. If you want to run through the shell script then you have to fire the command at your UNIX box.