Data Warehouse Architecture | Lecture #6 | Data Warehouse Tutorial for beginners

Summary

This 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

• 📊 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.
• 🔍 Business analysts use the data warehouse to measure performance and make critical adjustments to outperform competitors in the market.
• 🚀 Having a data warehouse offers advantages such as quick and efficient information gathering, which can enhance business productivity and impact long-term growth.
• 📈 A data warehouse provides a consistent view of customers and items, aiding in managing customer relationships and reducing costs by tracking trends over time.
• 🛠 To design an effective data warehouse, it is essential to understand and analyze business needs and construct a business analysis framework.
• 🔑 Different views on designing a data warehouse include the top-down view, data source view, data warehouse view, and business query view.
• 🏢 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).
• 🌐 The bottom tier involves backend tools and utilities for data extraction, cleaning, loading, and refreshing.
• 📊 The middle tier can implement OLAP servers in two ways: relational OLAP (ROLAP) or multi-dimensional OLAP (MOLAP).
• 🛑 The top tier includes various tools for reporting, analysis, and data mining, catering to the needs of end-users.
• 🏭 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

00:00

📊 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.

05:01

🏢 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.

Keywords

💡Business Analysis Framework

The Business Analysis Framework is a structured approach to understanding the business needs and how to leverage data to meet those needs. In the context of the video, it is the method by which a business analyst extracts information from the data warehouse to measure performance and make critical adjustments. This framework is essential for gaining a competitive edge in the market, as it allows for informed decision-making based on data-driven insights.

💡Data Warehouse

A Data Warehouse is a large, centralized repository of data designed to support management's decisions. It is mentioned in the script as a system that can gather information quickly and efficiently, enhancing business productivity and having a significant impact on business growth over time. The data warehouse is the core subject of the video, with its architecture and models being the main focus of the lecture.

💡OLAP Servers

OLAP (Online Analytical Processing) Servers are part of the middle tier in a data warehouse architecture. They are used for analyzing and processing data in a multi-dimensional manner, which is crucial for complex decision support and business intelligence applications. The script discusses two types of OLAP servers: ROLAP (Relational OLAP) that extends relational database management systems, and MOLAP (Multi-Dimensional OLAP) that directly implements multi-dimensional data operations.

💡Three-Tier Architecture

The Three-Tier Architecture is a style of software architecture where the system is divided into three logical layers: the client layer, the middle tier, and the data layer. In the script, this architecture is applied to data warehouses, with the bottom tier being the data warehouse database server, the middle tier housing the OLAP servers, and the top tier containing client tools for querying, reporting, and analysis.

💡Data Mart

A Data Mart is a subset of a data warehouse that contains data specific to a particular group or department within an organization. The script explains that data marts are valuable for specific groups, such as quality control or manufacturing departments, as they provide focused data relevant to their needs. Data marts are smaller, more flexible, and can be quickly implemented compared to a full enterprise warehouse.

💡Enterprise Warehouse

An Enterprise Warehouse is a comprehensive data warehouse that collects and integrates information spanning an entire organization. It is designed to provide enterprise-wide data integration, which can range from a few gigabytes to several terabytes. The script mentions that the enterprise warehouse is a model that contrasts with the data mart, as it contains data relevant to the entire organization rather than a specific subset.

💡Operational Systems

Operational Systems are the day-to-day systems that run a business, such as financial systems, customer relationship management (CRM) systems, and inventory management systems. In the context of the video, operational systems are the source of data that is captured, stored, and managed for the data warehouse, providing the raw data that is essential for business analysis.

💡Fact Tables

Fact Tables are a key component of a data warehouse's data warehouse view. They are used to store measurable facts or events, often associated with dimensions that provide context to the facts. The script mentions fact tables in the context of the data warehouse view, which includes both fact and dimension tables, representing the structured data stored within the data warehouse.

💡Dimension Tables

Dimension Tables are used in data warehousing to provide context to the facts stored in fact tables. They often contain descriptive information, such as time, location, or product details. The script refers to dimension tables as part of the data warehouse view, which, along with fact tables, helps in representing the information stored within the data warehouse.

💡Business Query View

The Business Query View is the perspective of the data from the end-user's point of view. It is how the data in the data warehouse is seen and utilized by the business analysts and decision-makers. The script explains that this view is crucial for designing an effective data warehouse, as it ensures that the system meets the needs of the users who will be interacting with it.

💡Data Mining Tools

Data Mining Tools are software applications used to discover patterns and relationships within large datasets. In the script, these tools are mentioned as part of the top tier in the three-tier architecture of a data warehouse, where they are used for analysis and to extract valuable insights from the data stored in the warehouse.

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