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THREE DIFFERENT data TYPES: Dimensions, MEASURE, and DetailView is NOTHING but an alias and it can be used to resolve the loops in the universe.

Three different data types: Dimensions, Measure, and DetailView is nothing but an alias and it can be used to resolve the loops in the universe.

DATA validation is to make sure that the LOADED data is accurate and meets the BUSINESS REQUIREMENTS. Strategies are DIFFERENT methods followed to meet the validation requirements.

Data validation is to make sure that the loaded data is accurate and meets the business requirements. Strategies are different methods followed to meet the validation requirements.

Hierarchies are logical structures that USE ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be COMPOSED of more than one hierarchy. For example, in the product dimension, there might be two hierarchies--one for product categories and one for product suppliers.Dimension hierarchies also GROUP levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to VIEW different levels of GRANULARITY. This is one of the key benefits of a data warehouse.When designing hierarchies, you must consider the relationships in business structures. Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

Hierarchies are logical structures that use ordered levels as a means of organizing data. A hierarchy can be used to define data aggregation. For example, in a time dimension, a hierarchy might aggregate data from the month level to the quarter level to the year level. A hierarchy can also be used to define a navigational drill path and to establish a family structure.Within a hierarchy, each level is logically connected to the levels above and below it. Data values at lower levels aggregate into the data values at higher levels. A dimension can be composed of more than one hierarchy. For example, in the product dimension, there might be two hierarchies--one for product categories and one for product suppliers.Dimension hierarchies also group levels from general to granular. Query tools use hierarchies to enable you to drill down into your data to view different levels of granularity. This is one of the key benefits of a data warehouse.When designing hierarchies, you must consider the relationships in business structures. Hierarchies impose a family structure on dimension values. For a particular level value, a value at the next higher level is its parent, and values at the next lower level are its children. These familial relationships enable analysts to access data quickly.

BUS Schema is composed of a MASTER suite of confirmed DIMENSION and STANDARDIZED definition if FACTS.

BUS Schema is composed of a master suite of confirmed dimension and standardized definition if facts.

Every COMPANY has METHODOLOGY of their own. However, to name a few SDLC Methodology, AIM methodology is standard USED.

Every company has methodology of their own. However, to name a few SDLC Methodology, AIM methodology is standard used.

Conformed dimensions are the dimensions, which can be used ACROSS multiple Data Marts in COMBINATION with multiple facts TABLES accordingly.

Conformed dimensions are the dimensions, which can be used across multiple Data Marts in combination with multiple facts tables accordingly.

Basic difference is E-R MODELING will have logical and PHYSICAL MODEL. DIMENSIONAL model will have only physical model. E-R modeling is used for normalizing the OLTP database design.Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design.

Basic difference is E-R modeling will have logical and physical model. Dimensional model will have only physical model. E-R modeling is used for normalizing the OLTP database design.Dimensional modeling is used for de-normalizing the ROLAP/MOLAP design.

The FACT table CONSISTS of the Index keys of the dimension/look up tables and the measures. So WHENEVER we have the keys in a table. That it implies that the table is in the normal FORM.

The fact table consists of the Index keys of the dimension/look up tables and the measures. So whenever we have the keys in a table. That it implies that the table is in the normal form.

Junk dimension: Grouping of RANDOM flags and text ATTRIBUTES in a dimension and moving them to a separate SUB dimension. Degenerate Dimension: Keeping the control information on FACT table ex: Consider a Dimension table with fields like order number and order line number and have 1:1 relationship with Fact table, In this case this dimension is removed and the order information will be directly store.

Junk dimension: Grouping of Random flags and text attributes in a dimension and moving them to a separate sub dimension. Degenerate Dimension: Keeping the control information on Fact table ex: Consider a Dimension table with fields like order number and order line number and have 1:1 relationship with Fact table, In this case this dimension is removed and the order information will be directly store.

Both differed in the concept of building the data warehouse.According to Kimball, Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business OBJECTIVES for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence, a unified view of the ENTERPRISE can be obtained from the dimension modeling on a local departmental level.Inmon beliefs in creating a data warehouse on a subject-by-subject AREA basis. Hence, the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their NEEDS arise. Point-of-sale (POS) data can be added later if management decides it is NECESSARY.

Both differed in the concept of building the data warehouse.According to Kimball, Kimball views data warehousing as a constituency of data marts. Data marts are focused on delivering business objectives for departments in the organization. And the data warehouse is a conformed dimension of the data marts. Hence, a unified view of the enterprise can be obtained from the dimension modeling on a local departmental level.Inmon beliefs in creating a data warehouse on a subject-by-subject area basis. Hence, the development of the data warehouse can start with data from the online store. Other subject areas can be added to the data warehouse as their needs arise. Point-of-sale (POS) data can be added later if management decides it is necessary.

View - STORE the SQL statement in the database and LET you use it as a table. Every time you ACCESS the view, the SQL statement EXECUTES. Materialized view - stores the results of the SQL in table form in the database. SQL statement only executes once and after that every time you RUN the query, the stored result set is used. Pros include quick query results.

View - store the SQL statement in the database and let you use it as a table. Every time you access the view, the SQL statement executes. Materialized view - stores the results of the SQL in table form in the database. SQL statement only executes once and after that every time you run the query, the stored result set is used. Pros include quick query results.

DATA Mining is used for the estimation of future. For example, if we take a company/business ORGANIZATION, by using the concept of Data Mining, we can predict the future of business in terms of REVENUE (or) Employees (or) Customers (or) ORDERS etc.Traditional approaches use simple algorithms for estimating the future. However, it does not give accurate results when compared to Data Mining.

Data Mining is used for the estimation of future. For example, if we take a company/business organization, by using the concept of Data Mining, we can predict the future of business in terms of Revenue (or) Employees (or) Customers (or) Orders etc.Traditional approaches use simple algorithms for estimating the future. However, it does not give accurate results when compared to Data Mining.

Gathering business REQUIREMENTS>>Identifying SOURCES>>Identifying FACTS>>Defining DIMENSIONS>>Define Attributes>>Redefine Dimensions / Attributes>>Organize Attribute Hierarchy>>Define Relationship>>Assign Unique IDENTIFIERS.

Gathering business requirements>>Identifying Sources>>Identifying Facts>>Defining Dimensions>>Define Attributes>>Redefine Dimensions / Attributes>>Organize Attribute Hierarchy>>Define Relationship>>Assign Unique Identifiers.

When you ADD a relational or a flat file SOURCE DEFINITION to a MAPPING, you need to connect it to a Source Qualifier transformation. The Source Qualifier represents the ROWS that the Informatica Server reads when it executes a session.

When you add a relational or a flat file source definition to a mapping, you need to connect it to a Source Qualifier transformation. The Source Qualifier represents the rows that the Informatica Server reads when it executes a session.

Static variable is not CREATED on FUNCTION stack but is created in the initialized data segment and hence the variable can be shared across the multiple call of the same function. Usage of static variables WITHIN a function is not thread safe.On the other hand, LOCAL variable or auto variable is created on function stack and valid only in the context of the function call and is not shared across function calls.

Static variable is not created on function stack but is created in the initialized data segment and hence the variable can be shared across the multiple call of the same function. Usage of static variables within a function is not thread safe.On the other hand, local variable or auto variable is created on function stack and valid only in the context of the function call and is not shared across function calls.

TYPES of DATA warehousing are:

1. Enterprise Data warehousing
2. ODS (Operational Data Store)
3. Data Mart

Types of data warehousing are:

1. Enterprise Data warehousing
2. ODS (Operational Data Store)
3. Data Mart

Fact is key PERFORMANCE indicator to ANALYZE the business. DIMENSION is USED to analyze the fact. Without dimension there is no meaning for fact.

Fact is key performance indicator to analyze the business. Dimension is used to analyze the fact. Without dimension there is no meaning for fact.

There is no data type for a SURROGATE KEY. REQUIREMENT of a surrogate Key: UNIQUE RECOMMENDED data type of a Surrogate key is NUMERIC.

There is no data type for a Surrogate Key. Requirement of a surrogate Key: UNIQUE Recommended data type of a Surrogate key is NUMERIC.

STAR schema: A single FACT table with N NUMBER of DimensionSnowflake schema: Any DIMENSIONS with extended dimensions are known as SNOWFLAKE schema.

Star schema: A single fact table with N number of DimensionSnowflake schema: Any dimensions with extended dimensions are known as snowflake schema.

Cubes are LOGICAL representation of MULTIDIMENSIONAL data. The edge of the cube contains DIMENSION members and the body of the cube contains data values.

Cubes are logical representation of multidimensional data. The edge of the cube contains dimension members and the body of the cube contains data values.

STAR schema contains the dimension TABLES mapped around one or more fact tables. It is a renormalized model and no need to use complicated joins. Also queries results fast.Snowflake schema: It is the normalized form of Star schema. It contains in-depth joins, because the tables are split in to many PIECES. We can easily do modification DIRECTLY in the tables. We have to use complicated joins, since we have more tables.There will be some delay in processing the query.

Star schema contains the dimension tables mapped around one or more fact tables. It is a renormalized model and no need to use complicated joins. Also queries results fast.Snowflake schema: It is the normalized form of Star schema. It contains in-depth joins, because the tables are split in to many pieces. We can easily do modification directly in the tables. We have to use complicated joins, since we have more tables.There will be some delay in processing the query.

Dimensional MODELLING is a design CONCEPT used by many data warehouse designers to build their data warehouse. In this design MODEL all the data is STORED in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the BUSINESS and the dimension table contains the context of measurements i.e., the dimensions on which the facts are calculated.

Dimensional Modelling is a design concept used by many data warehouse designers to build their data warehouse. In this design model all the data is stored in two types of tables - Facts table and Dimension table. Fact table contains the facts/measurements of the business and the dimension table contains the context of measurements i.e., the dimensions on which the facts are calculated.

The POINT at which ORACLE ends writing to ONE online redo log FILE and begins writing to another is CALLED a log switch.

The point at which ORACLE ends writing to one online redo log file and begins writing to another is called a log switch.

The On-line Redo Log is a set of TOW or more on-line redo files that RECORD all COMMITTED changes made to the database. Whenever a transaction is committed, the corresponding redo entries TEMPORARILY stores in redo log BUFFERS of the SGA are written to an on-line redo log file by the background process LGWR. The on-line redo log files are used in cyclical fashion.

The On-line Redo Log is a set of tow or more on-line redo files that record all committed changes made to the database. Whenever a transaction is committed, the corresponding redo entries temporarily stores in redo log buffers of the SGA are written to an on-line redo log file by the background process LGWR. The on-line redo log files are used in cyclical fashion.

All the DEFAULT storage parameters defined for the table SPACE can be changed USING the ALTER TABLESPACE command. When objects are created their INITIAL and MINEXTENS values cannot be changed.

All the default storage parameters defined for the table space can be changed using the ALTER TABLESPACE command. When objects are created their INITIAL and MINEXTENS values cannot be changed.

START an INSTANCE, MOUNT the DATABASE and OPEN the Database.

Start an instance, Mount the Database and Open the Database.

Rolling forward to recover data that has not been recorded in data files yet has been recorded in the on-line redo log, INCLUDING the contents of rollback segments. Rolling back TRANSACTIONS that have been explicitly rolled back or have not been COMMITTED as indicated by the rollback segments regenerated in step a.

1) Releasing any resources (LOCKS) HELD by transactions in process at the time of the failure.
2) Resolving any pending distributed transactions undergoing a two-phase commit at the time of the instance failure.

Rolling forward to recover data that has not been recorded in data files yet has been recorded in the on-line redo log, including the contents of rollback segments. Rolling back transactions that have been explicitly rolled back or have not been committed as indicated by the rollback segments regenerated in step a.

1) Releasing any resources (locks) held by transactions in process at the time of the failure.
2) Resolving any pending distributed transactions undergoing a two-phase commit at the time of the instance failure.

No

No

Exclusive Mode If the first INSTANCE that mounts a DATABASE does so in exclusive mode, only that Instance can mount the database. Parallel Mode If the first instance that mounts a database is STARTED in parallel mode, other instances that are started in parallel mode can ALSO mount the database.

Exclusive Mode If the first instance that mounts a database does so in exclusive mode, only that Instance can mount the database. Parallel Mode If the first instance that mounts a database is started in parallel mode, other instances that are started in parallel mode can also mount the database.

COMPLETE database recovery from DISK failure is POSSIBLE only in ARCHIVELOG MODE. ONLINE database backup is possible only in ARCHIVELOG mode.

Complete database recovery from disk failure is possible only in ARCHIVELOG mode. Online database backup is possible only in ARCHIVELOG mode.

CLOSE the DATABASE; DISMOUNT the Database and SHUTDOWN the INSTANCE.

Close the Database; Dismount the Database and Shutdown the Instance.

ARCHIVED Redo LOG consists of Redo Log FILES that have archived before being REUSED.

Archived Redo Log consists of Redo Log files that have archived before being reused.

An instance can be STARTED in (or later altered to be in) restricted mode so that when the database is open CONNECTIONS are LIMITED only to those WHOSE user ACCOUNTS have been granted the RESTRICTED SESSION system privilege.

An instance can be started in (or later altered to be in) restricted mode so that when the database is open connections are limited only to those whose user accounts have been granted the RESTRICTED SESSION system privilege.

A Partial Backup is any OPERATING system backup short of a FULL backup, taken while the database is OPEN or SHUT down.

A Partial Backup is any operating system backup short of a full backup, taken while the database is open or shut down.

A mirrored on-line REDO log consists of copies of on-line redo log files physically located on separate disks; CHANGES MADE to ONE member of the group are made to all MEMBERS.

A mirrored on-line redo log consists of copies of on-line redo log files physically located on separate disks; changes made to one member of the group are made to all members.

A full BACKUP is an operating SYSTEM backup of all data files, on- line redo LOG files and control FILE that constitute ORACLE DATABASE and the parameter.

A full backup is an operating system backup of all data files, on- line redo log files and control file that constitute ORACLE database and the parameter.

Yes

Yes

Yes

Yes

Yes

Yes

When an instance of an ORACLE database is STARTED, its control file is used to IDENTIFY the database and redo LOG files that must be OPENED for database operation to proceed. It is also used in database recovery.

When an instance of an ORACLE database is started, its control file is used to identify the database and redo log files that must be opened for database operation to proceed. It is also used in database recovery.

VIEWS do not CONTAIN or STORE DATA.

Views do not contain or store data.

UPDATE And Delete RESTRICT - A referential integrity RULE that disallows the update or deletion of referenced data. DELETE CASCADE - When a referenced ROW is deleted all associated dependent rows are deleted.

Update And Delete Restrict - A referential integrity rule that disallows the update or deletion of referenced data. DELETE Cascade - When a referenced row is deleted all associated dependent rows are deleted.

There are TWO TYPES of SYNONYMS PRIVATE and PUBLIC.

There are two types of Synonyms Private and Public.

The SET of REDO Log files YSDATE, UID, USER or USERENV SQL functions, or the pseudo columns LEVEL or ROWNUM.

The set of Redo Log files YSDATE, UID, USER or USERENV SQL functions, or the pseudo columns LEVEL or ROWNUM.

Each INDEX has an Index SEGMENT that STORES all of its DATA.

Each Index has an Index segment that stores all of its data.

Each databases LOGICALLY divided into ONE or more table spaces one or more data files are EXPLICITLY created for each table space.

Each databases logically divided into one or more table spaces one or more data files are explicitly created for each table space.

DATA SEGMENT,
INDEX Segment,
ROLLBACK Segment
and
TEMPORARY Segment.

Data Segment,
Index Segment,
Rollback Segment
and
Temporary Segment.

CLUSTERS are groups of one or more tables PHYSICALLY STORES together to SHARE common columns and are often USED together.

Clusters are groups of one or more tables physically stores together to share common columns and are often used together.

An integrity CONSTRAINT is a DECLARATIVE WAY to DEFINE a business rule for a column of a table.

An integrity constraint is a declarative way to define a business rule for a column of a table.

A table is the basic UNIT of DATA STORAGE in an ORACLE database. The TABLES of a database HOLD all of the user accessible data. Table data is stored in rows and columns.

A table is the basic unit of data storage in an ORACLE database. The tables of a database hold all of the user accessible data. Table data is stored in rows and columns.