Data Warehouse Architecture | Lecture #6 | Data Warehouse Tutorial for beginners
Summary
TLDRThis video delves into the business analysis framework and architecture of data warehouses. It highlights the advantages of data warehouses in enhancing business productivity, providing a consistent view of customer data, and reducing costs by tracking trends. The script explains different views for designing a data warehouse, outlines the three-tier architecture, and discusses various data warehouse models like virtual warehouse, data mart, and enterprise warehouse, emphasizing their roles in fulfilling organizational data needs.
Takeaways
- đ The lecture focuses on the business analysis framework and architecture of a data warehouse, aiming to provide a deep understanding of its construction and operation.
- đ Business analysts use the data warehouse to measure performance and make critical adjustments to outperform competitors in the market.
- đ Having a data warehouse offers advantages such as quick and efficient information gathering, which can enhance business productivity and impact long-term growth.
- đ A data warehouse provides a consistent view of customers and items, aiding in managing customer relationships and reducing costs by tracking trends over time.
- đ To design an effective data warehouse, it is essential to understand and analyze business needs and construct a business analysis framework.
- đ Different views on designing a data warehouse include the top-down view, data source view, data warehouse view, and business query view.
- đą The three-tier data warehouse architecture consists of the bottom tier (data warehouse database server), middle tier (OLAP servers), and top tier (end client layer with query and reporting tools).
- đ The bottom tier involves backend tools and utilities for data extraction, cleaning, loading, and refreshing.
- đ The middle tier can implement OLAP servers in two ways: relational OLAP (ROLAP) or multi-dimensional OLAP (MOLAP).
- đ The top tier includes various tools for reporting, analysis, and data mining, catering to the needs of end-users.
- đ Data warehouse models include virtual warehouse, data mart, and enterprise warehouse, each serving different organizational needs and data scopes.
Q & A
What is the primary purpose of a data warehouse in business analysis?
-The primary purpose of a data warehouse in business analysis is to provide the business analyst with information to measure performance, make critical adjustments, and gain a competitive advantage in the market.
What are some advantages of having a data warehouse for a business?
-Some advantages of having a data warehouse include the ability to gather information quickly and efficiently, enhancing business productivity, providing a consistent view of customers and items, and reducing costs by tracking trends and patterns over a long period in a consistent and reliable manner.
What is a business analysis framework in the context of a data warehouse?
-A business analysis framework in the context of a data warehouse is a structured approach to understanding and analyzing business needs to construct an effective and efficient data warehouse.
What are the different views that may be considered when designing a data warehouse?
-When designing a data warehouse, different views that may be considered include the top-down view, data source view, data warehouse view, and business query view.
What does the top-down view represent in data warehouse design?
-The top-down view in data warehouse design represents the selection of relevant information needed for the data warehouse.
What is the role of the data source view in the context of a data warehouse?
-The data source view represents the information being captured, stored, and managed by operational systems.
What does the data warehouse view include and what does it represent?
-The data warehouse view includes fact tables and dimension tables, representing the information stored inside the data warehouse, similar to metadata.
What is the business query view and how does it differ from other views?
-The business query view is the perspective of the data from the end-user's viewpoint, differing from other views by focusing on how the end user interacts with and queries the data.
Can you describe the three-tier architecture of a data warehouse?
-The three-tier architecture of a data warehouse consists of the bottom tier (data warehouse database server), the middle tier (OLAP servers), and the top tier (end client layer with query, reporting, analysis, and data mining tools).
What are the two types of OLAP servers mentioned in the script and how do they differ?
-The two types of OLAP servers are ROLAP (Relational OLAP), which extends a relational database management system to map operations on multi-dimensional data to standard relational operations, and MOLAP (Multi-dimensional OLAP), which directly implements multi-dimensional data and operations.
What are the different data warehouse models mentioned in the script?
-The different data warehouse models mentioned are the virtual warehouse, data mart, and enterprise warehouse.
What is a virtual warehouse and how is it built?
-A virtual warehouse is a view over an operational data store and is built by utilizing the excess capacity on the operational database servers.
What is a data mart and how does it serve specific groups within an organization?
-A data mart contains a subset of organization-wide data that is valuable for specific groups within an organization. It is designed to meet the particular data needs of those groups, such as quality control, manufacturing, or research and development departments.
What is an enterprise warehouse and what does it provide?
-An enterprise warehouse collects and integrates information and subjects spanning an entire organization, providing enterprise-wide data integration from both operational systems and external information providers.
Outlines
đ Introduction to Business Analysis Framework for Data Warehouses
This paragraph introduces the topic of business analysis in the context of data warehouses. It emphasizes the importance of understanding the architecture of a data warehouse to leverage its full potential. The speaker outlines the advantages of a data warehouse, such as quick and efficient information gathering, which can enhance business productivity and growth. The paragraph also touches on the need for a business analysis framework to measure performance and make critical adjustments to outperform competitors. Different views on designing a data warehouse are mentioned, including the top-down view, data source view, data warehouse view, and business query view, each serving a unique purpose in the overall design and operation of a data warehouse.
đą Exploring the Three-Tier Data Warehouse Architecture
The second paragraph delves into the three-tier architecture of a data warehouse, which is fundamental to its operation. The bottom tier consists of the data warehouse database server, which is the hardware backbone that supports the system. The middle tier is home to OLAP (Online Analytical Processing) servers, which can be implemented in two ways: ROLAP (Relational OLAP) that extends relational database management systems, and MOLAP (Multi-dimensional OLAP) that directly implements multi-dimensional data operations. The top tier is the end-client layer, equipped with various tools for querying, reporting, analysis, and data mining. The paragraph also introduces different types of data warehouse models, including the virtual warehouse, data mart, and enterprise warehouse, each serving different organizational needs and purposes.
Mindmap
Keywords
đĄBusiness Analysis Framework
đĄData Warehouse
đĄOLAP Servers
đĄThree-Tier Architecture
đĄData Mart
đĄEnterprise Warehouse
đĄOperational Systems
đĄFact Tables
đĄDimension Tables
đĄBusiness Query View
đĄData Mining Tools
Highlights
Introduction to the business analysis framework of the data warehouse and its architecture.
Discussion on the advantages of having a data warehouse, including quick and efficient information gathering to enhance business productivity.
The data warehouse's role in providing a consistent view of customers and items for better customer relationship management.
How a data warehouse helps in reducing costs by tracking trends and patterns over a long period in a consistent and reliable manner.
The necessity to understand and analyze business needs for designing an effective and efficient data warehouse.
Different views on designing a data warehouse: top-down view, data source view, data warehouse view, and business query view.
Explanation of the top-down view, focusing on the selection of relevant information needed for a data warehouse.
Description of the data source view, which involves information captured, stored, and managed by operational systems.
Details on the data warehouse view, including fact tables and dimension tables that represent stored information.
Business query view, which is the end user's perspective on the data within the data warehouse.
Overview of the three-tier data warehouse architecture: bottom tier, middle tier, and top tier.
The bottom tier consists of the data warehouse database server and the hardware required for its operation.
Middle tier includes OLAP servers, with options for relational OLAP and multi-dimensional OLAP models.
Top tier features end client layer with query, reporting, analysis, and data mining tools.
Diagrammatic representation of the three-tier architecture of a data warehouse.
Different data warehouse models: virtual warehouse, data mart, and enterprise warehouse.
Virtual warehouse as a view over an operational data warehouse, requiring excess capacity on operational database servers.
Data mart as a subset of organization-wide data, valuable for specific groups within an organization.
Enterprise warehouse collecting information spanning an entire organization for enterprise-wide data integration.
Upcoming lecture continuation on load manager, warehouse manager, query manager, and detailed information on summary information.
Transcripts
[Music]
hello everyone welcome to my channel
so in this lecture we are going to
discuss about the business analysis
framework
of the data warehouse and its
architecture
in detail so the previous lecture we
have seen
the different processes which are
involved in a data warehouse
so in this lecture we are going to have
a deep dive into the architecture of a
data warehouse
in brief to get to the proper
understanding how the data
warehouse is built so without further
ado
let's get into it so our first topic is
business analysis framework so the
business analyst
gets the information from the data
warehouse
to measure the performance and make the
critical adjustment
in order to win over the other business
holders in the market
so having the data warehouse has many
advantages
that we have also discussed in the
previous lectures
so one of them is since the data
warehouse can gather the information
very quickly and more efficiently it can
enhance the business
productivity and in the long term it
will have a greater impact on the
business
growth our next advantage is a data
warehouse provides
a consistent view of a customer and
items
hence it is help us to manage the
customer relationship
and also a data warehouse helps in
bringing down the cost by tracking the
trends
patterns over the long period in a
consistent
and very reliable manner so this
advantage
helps for the business growth as well as
to excel over the other businesses which
are present in the market
so to design an effective and efficient
data warehouse
we need to understand and analyze the
business needs
and construct a business analysis
framework
but the each person may have different
views
regarding how to design a data warehouse
so these views
can be the top down view data source
view
the data warehouse view and the business
query view
so you might ask what they really means
so the top down view is nothing but
the view which allows the selection of
relevant information which is needed for
a data warehouse
the data source view is nothing but the
information being captured
stored and managed by the operational
systems
the next one is data warehouse view it
is nothing but the view which includes
the fact tables
and the dimension tables it represents
the information which is stored
inside the data warehouse just like the
metadata that we have discussed in the
previous lectures
and the last one is business query view
so it is the view
of the data from the viewpoint of end
user
our next topic is the three tier data
warehouse architecture
so generally a data warehouse adopts a
three-tier architecture
so these are the three tiers of a data
warehouse one is a bottom tier
next one is a middle tier and a top tier
so we'll discuss what are they in brief
so the first one is
bottom tier so the bottom tier of
architecture
is the data warehouse database server
and server is nothing but the hardware
which is required to operate the data
warehouse
so it is the relational database system
we use the backend tools and the
utilities
to feed the data into the bottom tier
so this backend tools and utilities
performs the extraction
cleaning and loading the data and also
refreshing the functions
we have already discussed how the
extract
clean and load process happens in the
data warehouse
so if you want to know more please refer
our data warehouse tutorial
which is given in the link in the
description and also in the i button
here
our next tier is middle tier in the
middle tier we have the
olap servers so there are two ways to
implement
the olap servers first one is relational
olap or we can say it is roll
which is an extended relational database
management system
so the rollup maps the operations on the
multi-dimensional data
to standard the relational operations
and the next one is multi-dimensional
overlap or a molab model
which directly implements the
multi-dimensional data
and the operations and our last tier is
top tier so this tier is the end client
layer
so this layer holds the query tools and
the reporting tools
analysis tools and the data mining tools
so there are
different reporting tools you might
heard before
just like a power desktop or informatica
tool
which is used for reporting and analysis
so the following diagram shows the
three-tier architecture of a data
warehouse
so here you can see the bottom tier
which in which we have the olap servers
which is nothing but a data warehouse
database server
we have the middle tier which have the
olap server
and the last tier which is a top tier
which contains the queries
and reporting tools also the analysis
and the data mining tools
our next topic is data warehouse models
so from the perspective of data
warehouse architecture
we have the following data warehouse
model which are
virtual warehouse data mart and
the enterprise warehouse so we have
already discussed what is a data mart
its significance in detail so our first
model is
virtual warehouse so view over
an operational data warehouse is known
as a virtual warehouse
it is very easy to build a virtual
warehouse building a virtual warehouse
requires the excess capacity
on the operational database servers
which is nothing but
the servers which operates the data
warehouse
the next one is data mart which you are
already familiar with
so the data mart contains a subset of
organization-wide data
so the subset of data is valuable for
specific groups
of an organization so the manufacturing
field
there may be several groups such as
quality control departments
manufacturing departments and research
and development departments
so these different groups require
different types of data
and different type of data marts which
contains the relevant data
which is focused on a specific groups of
people
so in other words we can claim that the
data marks contain the data
for a specific particular group but you
have to remember
some points clearly regarding the data
marks
so the windows base or unix or linux
based servers
are used to implement the data markets
they are implemented on a low
cost server which make it more
economical
the implementation of a data mart cycles
is measured
in a short-term periods of time that is
in the weeks
rather than months or years the life
cycle of data marts
may be complex in long run if its
planning
and design are not organization wide
also the data marts are customized by
department
so the different departments can
customize the
data mods as per the requirements
the next point is the source of a data
mart is
departmentally structured data warehouse
this is very important as well as the
data marks are very flexible
which makes them versatile to cope up
with the changes
which are made according to the
requirements of a user
and the last point you have to remember
is data marks are very small in size
and our last data warehouse model is a
enterprise warehouse
so an enterprise warehouse collects all
the information
and the subjects spanning an entire
organization
it provides us the enterprise-wide data
integration
the data is integrated from a
operational systems
and the external information providers
this information can vary from a few
gigabytes to a hundreds of gigabytes
terabytes or beyond so these are nothing
but the enterprise warehouses
so i hope you understood what is a
business
analysis framework in a data warehouse
and why we are using the data warehouse
in the first place
and we have also seen the three-tier
data warehouse
architecture in detail and at last we
have seen the data warehouse models
in brief so i hope you got a clear idea
how the data warehouse is built
according to the requirement of an
organization
so the next lecture will be continuation
of this lecture
where we will discuss the load manager
warehouse manager
query manager as well as the detailed
information
and summary information in detail if you
like this video
please subscribe to my channel and ring
the notification bell
to get the latest updates thanks for
watching
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