Introduction :

A data warehouse is a centralized repository for storing and managing large

amounts of data from various sources for analysis and reporting. It is
optimized for fast querying and analysis, enabling organizations to make
informed decisions by providing a single source of truth for data. Data
warehousing typically involves transforming and integrating data from
multiple sources into a unified, organized, and consistent format.
Prerequisite – Data Warehousing Data warehouse can be controlled when
the user has a shared way of explaining the trends that are introduced as
specific subject. Below are major characteristics of data warehouse :

1. Subject-oriented – A data warehouse is always a subject oriented as it

delivers information about a theme instead of organization’s current
operations. It can be achieved on specific theme. That means the data
warehousing process is proposed to handle with a specific theme which is
more defined. These themes can be sales, distributions, marketing etc.
A data warehouse never put emphasis only current operations. Instead, it
focuses on demonstrating and analysis of data to make various decision.
It also delivers an easy and precise demonstration around particular
theme by eliminating data which is not required to make the decisions.
2. Integrated – It is somewhere same as subject orientation which is made
in a reliable format. Integration means founding a shared entity to scale
the all similar data from the different databases. The data also required to
be resided into various data warehouse in shared and generally granted
manner.
 A data warehouse is built by integrating data from various sources of data
such that a mainframe and a relational database. In addition, it must have
reliable naming conventions, format and codes. Integration of data
warehouse benefits in effective analysis of data. Reliability in naming
conventions, column scaling, encoding structure etc. should be confirmed.
Integration of data warehouse handles various subject related warehouse.
3. Time-Variant – In this data is maintained via different intervals of time
such as weekly, monthly, or annually etc. It founds various time limit
which are structured between the large datasets and are held in online
transaction process (OLTP). The time limits for data warehouse is wide-
ranged than that of operational systems. The data resided in data
warehouse is predictable with a specific interval of time and delivers
information from the historical perspective. It comprises elements of time
explicitly or implicitly. Another feature of time-variance is that once data is
stored in the data warehouse then it cannot be modified, alter, or updated.
Data is stored with a time dimension, allowing for analysis of data over
time.
4. Non-Volatile – As the name defines the data resided in data warehouse
is permanent. It also means that data is not erased or deleted when new
data is inserted. It includes the mammoth quantity of data that is inserted
into modification between the selected quantity on logical business. It
evaluates the analysis within the technologies of warehouse. Data is not
updated, once it is stored in the data warehouse, to maintain the historical
data.
In this, data is read-only and refreshed at particular intervals. This is
beneficial in analysing historical data and in comprehension the
functionality. It does not need transaction process, recapture and
concurrency control mechanism. Functionalities such as delete, update,
and insert that are done in an operational application are lost in data
warehouse environment. Two types of data operations done in the data
warehouse are:
 Data Loading
 Data Access
1. Subject Oriented: Focuses on a specific area or subject such as sales,
customers, or inventory.
2. Integrated: Integrates data from multiple sources into a single, consistent
format.
3. Read-Optimized: Designed for fast querying and analysis, with indexing
and aggregations to support reporting.
4. Summary Data: Data is summarized and aggregated for faster querying
and analysis.
5. Historical Data: Stores large amounts of historical data, making it
possible to analyze trends and patterns over time.
6. Schema-on-Write: Data is transformed and structured according to a
predefined schema before it is loaded into the data warehouse.
7. Query-Driven: Supports ad-hoc querying and reporting by business
users, without the need for technical support.
Functions of Data warehouse: It works as a collection of data and here is
organized by various communities that endures the features to recover the
data functions. It has stocked facts about the tables which have high
transaction levels which are observed so as to define the data warehousing
techniques and major functions which are involved in this are mentioned
below:
1. Data Consolidation: The process of combining multiple data sources into
a single data repository in a data warehouse. This ensures a consistent
and accurate view of the data.
2. Data Cleaning: The process of identifying and removing errors,
inconsistencies, and irrelevant data from the data sources before they are
integrated into the data warehouse. This helps ensure the data is
accurate and trustworthy.
3. Data Integration: The process of combining data from multiple sources
into a single, unified data repository in a data warehouse. This involves
transforming the data into a consistent format and resolving any conflicts
or discrepancies between the data sources. Data integration is an
essential step in the data warehousing process to ensure that the data is
accurate and usable for analysis. Data from multiple sources can be
integrated into a single data repository for analysis.
4. Data Storage: A data warehouse can store large amounts of historical
data and make it easily accessible for analysis.
5. Data Transformation: Data can be transformed and cleaned to remove
inconsistencies, duplicate data, or irrelevant information.
6. Data Analysis: Data can be analyzed and visualized in various ways to
gain insights and make informed decisions.
7. Data Reporting: A data warehouse can provide various reports and
dashboards for different departments and stakeholders.
8. Data Mining: Data can be mined for patterns and trends to support
decision-making and strategic planning.
9. Performance Optimization: Data warehouse systems are optimized for
fast querying and analysis, providing quick access to data.