Data Warehouse helps in what?

(a)  

Crucial process in data warehousing that

involves collecting data from different

sources, transforming it into a structured

format and loading it into a data

warehouse for analysis

(a)  

Designed to handle a large number of short, fast and

realtime transactions. These systems ensure data

integrity, speed and concurrency making them ideal

for application where frequent transactions occur,

such as banking,retail and airline bookings.

(a)  

Modern approach to data integration where raw data is first

loaded into a data warehouse before applying transformations.

ELT is commonly used in cloudbased data warehouses like

SNOWFLAKE. BIGQUERY, and AMAZON REDSHIFT,which have

powerful processing capabilities for large datasets.

(a)  

Is a centralized database that integrates data from multiple sources

to provide real-time or near-real-time operational reporting. Acts as

a bridge between OLTP and data warehouses helping organizations

make quick, data-driven decisions.

(a)  

serves the same role as a data warehouse, but it is

intentionally limited in scope.

(a)  

Exist to support daily operations. It is cleaned and validated, but not historically deep: it may be just the data for the current day

(a)  

Data warehouses are designed to

help you analyze data

(a)  

means that,

once entered into the data

warehouse, data should not

change.

(a)  

Data warehouses must put data

from disparate sources into a

consistent format.

(a)  

A data warehouse's focus on

change over time. This is very much in

contrast to online transaction

processing (OLTP) systems

(a)  

characteristics of a

data warehouse as set forth

by William Inmon:

(a)  

• - offers the industry's first and only embedded OLAP server

• - uses a multidimensional data model to

perform complex statistical, mathematical, and

financial analysis of historical data in real time.

• - is fully integrated in the database, so that you

can use standard SQL administrative, querying, and

reporting tools.

is more conceptual and abstract than the physical

design. You look at the logical relationships

among the objects.

(a)  

you look at the most

effective way of storing and retrieving the objects as well as handling

them from a transportation and backup/recovery perspective.

(a)  

involves identifying the things of

importance (entities), the properties of these things (attributes), and how they are related to

one another (relationships).

(a)  

Represents a real-world object

(a)  

Describe a property of an entity

(a)  

Stored as table in a database and Has a unique

identifier (primary key)

(a)  

Stored as a column in a table and can be unique or not

(a)  

is a collection of database objects, including tables, views, indexes,

and synonyms.

(a)  

defines the logical organization of data. It

helps in structuring how data is stored, related and retrieved for analysis.

(a)  

The most common types of schemas used in data warehousing are:

(a)  

is the simplest data warehouse schema.

(a)  

are the two types of objects commonly

used in dimensional data warehouse schemas.

(a)  

-Are the large tables in your data warehouse schema

-Typically contain facts and foreign keys to the dimension tables

-Represent data, usually numeric and additive, that can be analyzed and examined.

(a)  

• - Also known as lookup or reference tables, contain the relatively static data in the data warehouse.

• - Store the information you normally use to contain queries.

• - Are usually textual and descriptive and you can use them as the row headers of the result set.

are logical structures that use ordered levels to organize data.

(a)  

represents a position in a hierarchy. It range from general to

specific, with the root level as the highest or most general level.

(a)  

specify top-to-bottom ordering of levels from most

general (the root) to most a specific information. They define the parent-child

relationship between the levels in a hierarchy.

(a)  

• - are specified for one distinct record in a dimension table.

• - are often used to avoid the potential problem of unique identifiers changing.

  • - are represented with the # character.

is a logical structure that defines how data is organized and represented in a data warehouse

(a)  

three main types of schemas used in data warehouses:

(a)  

is similar to a star schema, but it includes additional normalization of the dimension tables. This can reduce data redundancy and save storage space, but it can make queries more complex.

(a)  

combines elements of both star and snowflake schemas, using a

combination of denormalized and normalized tables to optimize performance and reduce data redundancy.

(a)  

• - refers to a specific object or concept that is important to the organization's business operations.

• - is a characteristic or property of an entity that is stored and analyzed for the purpose of making informed business decisions.

- are often defined as columns in a table or view, and they represent the specific details that are associated with each instance of an entity.

Types of Attributes:

(a)  

are used to define the format and structure of the data that is stored in attributes.

(a)  

determines the type of data that can be stored in that attribute and how

the data is processed and analyzed.

(a)  

are used to store numeric values, such as integers or decimals

(a)  

are used to store text or string values, such as names or descriptions.

(a)  

are used to store values that represent dates or times

(a)  

are used to store values that represent true or false

(a)  

are used to store binary data, such as images or files

(a)  

common data types used in data warehouses include:

(a)  

are rules or conditions that are used to ensure that the data stored in the database is

accurate, consistent, and meets specific business requirements.

(a)  

Types of Constraint:

(a)  

are used to identify unique records within a table and to establish relationships between tables.

are used to ensure that data is consistent and accurate, and they help to optimize queries and data retrieval.

(a)  

is a column or set of columns that uniquely identifies each row in a table.

(a)  

is a column or set of columns in one table that refers to the primary key in another table.

(a)  

is a column or set of columns that could potentially serve as the primary key for a table.

(a)  

is a primary key that consists of multiple columns. The combination of columns must be

unique to identify each row in the table.

(a)  

is an artificially created primary key that is used in place of a natural primary key.

(a)  

is a connection between tables that enables data to be retrieved and analyzed across multiple tables.

(a)  

each row in one table is related to exactly one row in another table.

This type of relationship is not commonly used in data warehouses, as it is often more efficient to combine the tables into a single table.

(a)  

each row in one table can be related to multiple rows in another table.

(a)  

multiple rows in one table can be related to multiple rows in

another table. This type of relationship is typically implemented using a bridge or junction table that contains the keys from both tables.

(a)  

-Plays the role to improve the performance of data retrieval operations.

(a)  

They are used to enforce data quality and prevent data inconsistencies and errors.

(a)  

is a pre-computed table or summary of data from one or more tables in the

warehouse.

(a)  

used to organize and categorize data into meaningful and intuitive groupings.

(a)  

It is the process in a data warehouse involves retrieving data from various sources, transforming it to meet the requirements of the target system, and loading it into the data warehouse.

(a)  

involves retrieving data from various sources, transforming it to meet the requirements

of the target system, and loading it into the data warehouse.

(a)  

refers to the movement of data from the source systems to the data

warehouse.

(a)  

involves transferring the data from the staging area into the data warehouse.

(a)  

is a process used in data warehousing to capture

changes to data in source systems and replicate them in the data warehouse.

(a)  

common way of introducing data warehousing is to refer to the characteristics of a data warehouse as set forth by:

(a)