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Data Warehousing Back

         
  Course Developer   Course Overview  
 
Dr D V L N Somayajulu
Professor
Department of Computer Science
& Engineering
 
for further details click here
 
This Course aims to provide the basic concepts of Data Warehouse. This course is organized in the form of various modules, further divided into lectures. The topics that are covered in this course are Data Warehouse Fundamentals, Data Warehouse Architectures and Implementation options, ...  
Click here to view more details about the course  
         
  Course Plan   Course Status  
 
The course is divided into TEN modules.  
Click here for further details about the course.  

 
This part provides the status of each quadrant for each lecture by indicating the date of completion of the activity.  
Click here to see full details  
 
 
 
     



Course Developer

DVLN Somayajulu, currently working as a Professor in the department of Computer Science and Engineering at National Institute of Technology (formerly known as Regional engineering College), Warangal.  Also served as Head, Computer Centre and Head of Dept of CSE during 2006 and 2007-10 respectively. He joined Regional Engineering College, Warangal   in 1988 after completing his Masters Degree in Mathematics from Indian Institute of Technology, Kharagpur  during 1982-84  and M. Tech in Computer Science & Data Processing Indian Institute of Technology,  Kharagpur during 1985-87. Also he obtained AMIE in 1993 from The Institution of Engineers (India), Kolkata.

He obtained his Ph .D in Computer science & Engineering from Dept of CSE, IIT, Delhi in the area of  incomplete databases in 2002. He has more than 23 years of teaching experience in Computer Science & Engineering at NIT Warangal. Presented more than 30 research papers in National/International conferences and Journals.

Notable Achievements:

  • Technical expert committee member in  TEQIP –II  of  National Project Implementation Unit – a Govt of India Unit for World Bank Assisted Project for Technical Education.

  • Institute Level Coordinator for NMEICT activities at NIT, Warangal.
  • Invited to attend Microsoft Faculty Research Summit 2004 held at Microsoft Research Centre, Redmond, Seattle, USA as a Delegate among the 12 people invited from India and presented the progress of research projects.
  • Awarded DST, Govt. of India  research project under Scheme for Young scientists during 1998-2001
  • Designed and developed courseware for subjects  Oracle 8i and RDBMS and Oracle for IBM global Services Pvt limited, Bangalore.

  • Presented 15 research papers in the international and national conferences.

  • Visited Austria in 1998 for presenting a paper at 8th International Conference of Database and Expert Systems (DEXA98)

  • Organized Several workshops and MHRD STTPs.  

  • Completed tool development project on A tool for search engine marketing

  • Completed MCIT GOI sponsored research project entitled “ Design and development of learning system for intrusion Tolerant Database System” during 2006-08.

  • Co-author for the adaptation of the book entitled Fundamentals of Database systems by Elamasri and Navathe and published by Pearson Education in January 2006.

  • Organizing Secretary for 23rd Indian engineering Congress at NIT, Warangal and organized this event as part of Golden Jubilee celebrations.

  • Visited  University of Scottland-London,   Las Vegas- USA, Sandiego- USA, Singapore, China & Austria and presented  research papers.

Somayajulu




Course Status

This table provides the status of each quadrant for each lecture by indicating the date of completion of the activity.

Lecture
No

Lecture Title

Quadrant
1

Quadrant
2

Quadrant
3

Quadrant
4

 

Business Intelligence – Role of Data Warehousing in BI
L1 What is Business Intelligence? Why it is popular?
L2 Steps in Business Intelligence, BI cycle, BI tools and Advantages of Business Intelligence
L3 Role of Data Warehousing in BI, Limitations and Risks in Data Warehouse
L4 Why separate Data Warehouse, Data Warehouse Evolution
L5 KDD process and Data Warehouse Architecture
  Data Warehouse Fundamentals
L6 Data Warehouse Evolution, What is Data Warehouse, Uses of Data Warehouse
L7 Advantages of Data Warehouse, Types of Warehouses, and Roadmap to Data Warehousing
  Data Warehouse Implementation and architectural Options  - Part 1
L8 Data Warehouse  implementation methodology - stakeholder
L9 Data Warehouse Scope Definition
L10 Bank Case study - Discussion
L11 Data Warehouse architectural options
L12 Data Marts - Horizontal and vertical data marts
  Data Warehouse Implementation and architectural Options  - Part 2
L13 Why Data Warehouse projects fail?, Data Warehouse  resource requirements, Data Warehouse buy versus build
L14 Mistakes to avoid,  Cost factors for Data Warehouse.
 

Data pre-processing – 1
(Summarization and Cleaning Methods)

L15 Why to pre-process data, different forms of data processing.
L16 Descriptive data summarization methods
L17 Data cleaning methods
  Data pre-processing – 2
(Data Integration, transformationetc.)
L18 Data Integration methods and data transformation methods. 
L19 Data reduction and  data discretization methods
L20 Concept hierarchy generation methods
  ETL Overview
L21 Introduction on What is ETL?, driving needs of ETL, benefits of ETL
L22 ETL in OLTP and OLAP integration, ETL life cycle, ETL architecture
L23 Overview of ETL tools
L24 Case study on ETL integration and transformation
  Principles of Dimensional Modeling - Concepts
L25 Dimensional Modeling: Basic Building Blocks
L26 ER Modeling Vs Dimensional Modeling
L27 Star Schema model
L28 Dimension Tables & Fact Tables
L29 Dimensional Modeling : Interactive Part
  Principles of Dimensional Modeling  - Design and Case study
L30 Snowflake & Fact consellation schemas
L31 Aggregated fact tables
L32 Different types of Dimenions & Fact tables
L33 Data marts
  Introduction to Online Analytical Processing
L34 Introduction to OLAP, key features of OLAP, OLAP support in SQL.
L35 Comparison of OLAP with OLTP System
L36 Need for Multi Dimensional Analysis and Representation of Data in Multi Dimensional form
  Multi Dimensional Model
L37 Multi Dimensional Model
L38 Data cube, lattice and OLAP operators
  Relational DBMS support for OLAP
L39 Aggregations,  Data Cube Demonstration using SQL, Categories of OLAP tools
L40 Categories of OLAP
 

 

Course Overview

This Course aims to provide the basic concepts of Data Warehouse. This course is organized in the form of various modules, further divided into lectures. The topics that are covered in this course are Data Warehouse Fundamentals, Data Warehouse Architectures and Implementation options, Data Pre-processing Techniques, ETL overview, Dimensional Modeling, Case study on Dimensional modeling, Online Analytical processing, and Relational  DBMS support for OLAP.  

Data warehouse was born as a place where relevant data could be held for identifying strategic reports for management. Many large businesses found themselves with data scattered across multiple platforms and variations of technology, making it almost impossible for any one individual to use data from multiple sources. A key idea within data warehousing is to take data from multiple platforms/technologies and place them in a common location that uses a common querying tool. In this way operational databases could be held on whatever system was most efficient for the operational business, while the reporting / strategic information could be held in a common location using a common language. All of this was designed to make decision support more readily available and without affecting day to day operations. Data that is interesting will assist decision makers in making strategic decisions relative to the organization's overall mission.

The first step in building data warehouse is data modeling. Data modeling is the arrangement, classification and categorization of data using a Data structures. Data warehouses often hold large amounts of information which are sometimes subdivided into smaller logical units called dependent data marts. Dependent Data marts allow for easier reporting by keeping relevant data together in one location. Periodically, one imports data from enterprise resource planning (ERP) systems and other related business software systems into the data warehouse for further processing. It is common practice to "stage" data prior to merging it into a data warehouse. In this sense, to "stage data" means to queue it for preprocessing, usually with an ETL tool. The preprocessing program reads the staged data (often a business's primary OLTP databases), performs qualitative preprocessing or filtering (including de-normalization, if deemed necessary), and writes it into the warehouse.

A data warehouse is created by analyzing ways to categorize data using dimensions and ways to summarize data using measures. Dimensions can be used to filter data by excluding results or by displaying data in different cells of a presentation. Measures are used to create averages and totals using pre-computed aggregates.
Business Intelligence reports (e.g., MIS reports) may then be generated from the data managed by the warehouse. In this way the data warehouse supplies the data for and supports the business intelligence tools that an organization might use

This Course provides the basic concepts of Data Warehouse. This course is organized in the form of various modules and lectures. The topics that are covered in this course are Data Warehouse fundamentals, Data Warehouse architectures and implementation options,  Data Pre-processing techniques, ETL overview, dimensional modeling, case study on Dimensional modeling, Online Analytical processing, Relational  DBMS support for OLAP,  Data Generalization methods, and overview of Data Warehouse administration. 

Click here for more details about various lectures on Data Warehousing

Course Plan

There are ten modules in this course. The following table lists the details of title and sub topics covered in each lecture of these modules.

Lesson No. Topics Covered Module Name Video Lecture Title
1 What is Business Intelligence? Why it is popular? Data Warehousing in Business Intelligence (DVLN Somayajulu) Business Intelligence – Role of Data Warehousing in BI
2 Steps in Business Intelligence, BI cycle, BI tools and Advantages of Business Intelligence
3 Role of Data Warehousing in BI, Limitations and Risks in Data Warehouse
4 Why separate Data Warehouse, Data Warehouse Evolution
5 KDD process and Data Warehouse Architecture
6 Data Warehouse Evolution, What is Data Warehouse, Uses of Data Warehouse Data Warehouse Fundamentals

(DVLN Somayajulu)
Data Warehouse Fundamentals
7 Advantages of Data Warehouse, Types of Warehouses, and Roadmap to Data Warehousing

8

Data Warehouse  implementation methodology, stakeholders Data Warehouse Implementation Issues
(S Upendra Rao)
Data Warehouse Implementation and architectural Options  - Part I
9 Scope Definition – Functionality in scope and out of scope
10 Data Warehouse architectural options – centralized, distributed
11 Data Warehouse architectural options - horizontal, vertical and Data Mart.
12 Discussion on Bank case study
13 Why Data Warehouse projects fail?, Data Warehouse  resource requirements, Data Warehouse buy versus build Data Warehouse Implementation and architectural Options  - Part II
14 Mistakes to avoid,  Cost factors for Data Warehouse.
15 Why to pre-process data, different forms of data processing. Data pre-processing
(DVLN Somayajulu)
Data pre-processing – 1 (Summarization and Cleaning Methods)
16 Data quality improvement methods, descriptive data summarization methods
17 Data cleaning methods
18 Data Integration methods, data transformation methods.  Data pre-processing – 2 (Data Integration, transformation etc.)
19 Data reduction and  data discretization methods
20 Concept hierarchy generation methods
21 What is ETL?, driving needs of ETL, benefits of ETL ETL Overview
(V Sreekumar)
ETL Overview
22 ETL in OLTP and OLAP integration, ETL life cycle, ETL architecture
23 Overview of ETL tools
24 Case study on ETL integration and transformation
25 Basic building blocks for dimensional modeling Dimensional Modeling
(DVLN Somayajulu)
Principles of Dimensional Modeling - concepts
26 Distinguish between dimensional modeling with Entity-Relationship modeling, Know about Facts, Dimensions and Hierarchies
27 Explore different types of dimension models, Learn Deeper Insights on Fact and Dimension tables
28 Case Study on Dimensional Modeling Case Study Design Case Study on Dimensional Modeling
29
30 Introduction to OLAP, comparison of OLAP with OLTP systems Online Analytical Processing (DVLN Somayajulu)  Introduction to Online Analytical Processing
31 Data Cube concept, Features of OLAP
32 Representation of Data in Multi Dimensional form and its associated operations
33

Multi Dimensional Model, Need for Multi Dimensional Analysis

34 OLAP operators Multi Dimensional Model
35 Relational DBMS support, OLAP operators
36 Data Cube Demonstration using SQL, Categories of OLAP tools Relational DBMS support for OLAP
37 Case Study on OLAP

 

 

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