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There are three types of SCD and they are as follows:

1. SCD 1 – The NEW record replaces the original record.
2. SCD 2 – A new record is added to the existing CUSTOMER DIMENSION table.
3. SCD 3 – A original data is modified to include new data.

There are three types of SCD and they are as follows:

OLAP is abbreviated as Online ANALYTICAL Processing, and it is SET to be a SYSTEM which collects, manages, PROCESSES multi-dimensional DATA for analysis and management purposes.

OLAP is abbreviated as Online Analytical Processing, and it is set to be a system which collects, manages, processes multi-dimensional data for analysis and management purposes.

OPERATIONAL systems are optimized to preserve the data integrity of the system, whereas data warehouse are optimized to SPEED up the process of data analysis. 

Operational system increases the speed of the business transactions through the use of normalization of the database and using the entity relationship models, whereas data warehouse uses de-normalization and dimension based model to speed the data retrieval. 

Operational system uses relational databases to maintain the relationship between the tables. It also consists of insert and update process that takes very less TIME hence INCREMENT in the performance of the system to create the TRANSACTION. Whereas, data warehouse store the same data multiple times to keep the aggregation of the data and gather the data from the operational systems.

Operational systems are optimized to preserve the data integrity of the system, whereas data warehouse are optimized to speed up the process of data analysis. 

Operational system increases the speed of the business transactions through the use of normalization of the database and using the entity relationship models, whereas data warehouse uses de-normalization and dimension based model to speed the data retrieval. 

Operational system uses relational databases to maintain the relationship between the tables. It also consists of insert and update process that takes very less time hence increment in the performance of the system to create the transaction. Whereas, data warehouse store the same data multiple times to keep the aggregation of the data and gather the data from the operational systems.

The key features that are in the chameleon are:

1. The chameleon method determines the PAIR of similar sub-clusters that can be connected with other clusters. It also finds the CLOSENESS of the clusters from one another. 
2. The chameleon with the above property overcomes the limitation that is present in agglomerative hierarchical MODEL. 
3. It uses different methods to take the internal characteristics of the clusters and matches with those which are already present. 
4. It doesn't depend on static model that is supplied by the user and uses AUTOMATED FUNCTIONS to perform the merging of the clusters that are already associated in the cluster.

The key features that are in the chameleon are:

NORMALIZATION HELPS in REDUCING DATA REDUNDANCY.

Normalization helps in reducing data redundancy.

PARTITIONING is DONE for various reasons such as EASY management, to assist BACKUP recovery, to ENHANCE performance.

Partitioning is done for various reasons such as easy management, to assist backup recovery, to enhance performance.

Fact table allows the measurement and the values of the FACTS of the data to be contained inside the table. This table consists of the foreign keys and primary keys of the dimension tables. It is located in between the star schema or snowflake schema. It provides values that are additive and independent VARIABLES through which the dimensional attributes are analyzed.

This table consists of the grains, which consist of atomic level of data and through which the facts in the tables are DEFINED. Each record defines the independent facts that provide higher level of data to be GIVEN to the user. It is useful in REPRESENTING the data due to easy storage and less memory to be taken to the facts of the data that are associated with it.

Fact table allows the measurement and the values of the facts of the data to be contained inside the table. This table consists of the foreign keys and primary keys of the dimension tables. It is located in between the star schema or snowflake schema. It provides values that are additive and independent variables through which the dimensional attributes are analyzed.

This table consists of the grains, which consist of atomic level of data and through which the facts in the tables are defined. Each record defines the independent facts that provide higher level of data to be given to the user. It is useful in representing the data due to easy storage and less memory to be taken to the facts of the data that are associated with it.

Core dimension is NOTHING but a Dimension table which is used as DEDICATED for SINGLE fact table or datamart.

Core dimension is nothing but a Dimension table which is used as dedicated for single fact table or datamart.

A load manager PERFORMS the OPERATIONS required to extract and load the PROCESS. The size and COMPLEXITY of load manager varies between specific SOLUTIONS from data warehouse to data warehouse.

A load manager performs the operations required to extract and load the process. The size and complexity of load manager varies between specific solutions from data warehouse to data warehouse.

FOLLOWING need to be DONE to start the DATABASE:

1. Start an INSTANCE.
2. Mount the database.
3. OPEN the database.

Following need to be done to start the database:

Time DIMENSIONS are usually loaded through all POSSIBLE dates in a year and it can be DONE through a program. Here, 100 YEARS can be represented with one row per DAY.

Time dimensions are usually loaded through all possible dates in a year and it can be done through a program. Here, 100 years can be represented with one row per day.

In DATAWAREHOUSING, loops are existing between the tables. If there is a loop between the tables, then the query generation will take more time and it CREATES AMBIGUITY. It is ADVISED to AVOID loop between the tables.

In datawarehousing, loops are existing between the tables. If there is a loop between the tables, then the query generation will take more time and it creates ambiguity. It is advised to avoid loop between the tables.

FOLLOWING are the ETL tools available:

1. Informatica.
2. DATA Stage.
3. Oracle.
4. Warehouse Builder.
5. Ab INITIO.
6. Data JUNCTION

Following are the ETL tools available:

A data warehouse helps to INTEGRATE data and store them historically so that we can analyze DIFFERENT aspects of business including, performance analysis, trend, PREDICTION etc. over a given time frame and use the RESULT of our analysis to improve the EFFICIENCY of business processes.

A data warehouse helps to integrate data and store them historically so that we can analyze different aspects of business including, performance analysis, trend, prediction etc. over a given time frame and use the result of our analysis to improve the efficiency of business processes.

A load MANAGER extracts data from the SOURCE system. FAST load the extracted data into temporary data store. Perform simple transformations into structure similar to the one in the data WAREHOUSE.

A load manager extracts data from the source system. Fast load the extracted data into temporary data store. Perform simple transformations into structure similar to the one in the data warehouse.

DIMENSIONAL Modeling is a concept which can be USED by dataware house designers to build their own datawarehouse. This model can be stored in two types of tables – Facts and Dimension table.

FACT table has facts and measurements of the business and dimension table CONTAINS the context of measurements.

Dimensional Modeling is a concept which can be used by dataware house designers to build their own datawarehouse. This model can be stored in two types of tables – Facts and Dimension table.

Fact table has facts and measurements of the business and dimension table contains the context of measurements.

FOLLOWING needs to be DONE when the database is SHUTDOWN:

• CLOSE the database.
• Dismount the database.
• Shutdown the INSTANCE.

Following needs to be done when the database is shutdown:

Dimensional model consists of dimension and fact tables. Fact tables STORE different transactional measurements and the foreign keys from dimension tables that qualifies the data. The GOAL of Dimensional model is not to achieve high degree of NORMALIZATION but to facilitate easy and faster data retrieval.

Ralph Kimball is one of the strongest PROPONENTS of this very popular data modeling technique which is often USED in many enterprise level data warehouses.

Dimensional model consists of dimension and fact tables. Fact tables store different transactional measurements and the foreign keys from dimension tables that qualifies the data. The goal of Dimensional model is not to achieve high degree of normalization but to facilitate easy and faster data retrieval.

Ralph Kimball is one of the strongest proponents of this very popular data modeling technique which is often used in many enterprise level data warehouses.

Foreign KEYS of DIMENSION tables are primary keys of ENTITY tables. Foreign keys of FACT tables are the primary keys of the dimension tables.

Foreign keys of dimension tables are primary keys of entity tables. Foreign keys of fact tables are the primary keys of the dimension tables.

Some applications include:

• financial SERVICES.
• banking services.
• CUSTOMER goods. 
• retail SECTORS.
• controlled manufacturing.

Some applications include:

Real-time datawarehousing CAPTURES the business DATA whenever it occurs. When there is business activity GETS completed, that data will be available in the flow and become available for use INSTANTLY.

Real-time datawarehousing captures the business data whenever it occurs. When there is business activity gets completed, that data will be available in the flow and become available for use instantly.

Name itself IMPLIES that it is a self explanatory term. CLEANING of ORPHAN records, Data breaching business rules, INCONSISTENT data and MISSING information in a database.

Name itself implies that it is a self explanatory term. Cleaning of Orphan records, Data breaching business rules, Inconsistent data and missing information in a database.

DATA ANALYTICS (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. A data warehouse is often BUILT to ENABLE Data Analytics

Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. A data warehouse is often built to enable Data Analytics