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Analysis Services Training

  1. MSAS - Browsing the Dependency Network
  2. MSAS - Building a Relational Decision Tree Model
  3. MSAS - Introduction to Data Mining
  4. MSAS - Applying security to a Dimension
  5. Tutorial 65: MSAS - Managing Cube Roles
  6. MSAS - Understanding Database Roles
  7. MSAS - Securing User Authentication
  8. MSAS - Introducing Analysis Services Security
  9. MSAS - Writebacks
  10. MSAS - Defining and Creating Drillthrough
  11. MSAS - Defining and Creating Auctions
  12. MSAS - Creating and Maintaining Calculated Members in Virtual Cubes
  13. MSAS - Building a Virtual Cube
  14. MSAS - Understanding Virtual Cubes
  15. MSAS - Introducing Solve Order
  16. MSAS - Implementing Calculations Using MDX Part 2
  17. MSAS - Implementing Calculations Using MDX Part 1
  18. MSAS - Merging Partitions
  19. MSAS - Introduction and Managing Partitions
  20. MSAS - Troubleshooting Cube Processing
  21. MSAS - Optimizing Cube Processing
  22. MSAS - Processing Dimensions and Cubes
  23. MSAS - Introducing Dimension and Cube Processing
  24. MSAS: Optimization Tuning Part 2
  25. MSAS: Optimization Tuning Part 1
  26. MSAS: Usage-Based Optimization
  27. MSAS: Analysis Services Aggregations
  28. MSAS: The Storage Design Wizard
  29. MSAS: Analysis Server Cube Storage
  30. MSAS: Defining Cube Properties
  31. MSAS: Introduction and Working with Measures
  32. MSAS: Introduction and Working with Cubes
  33. MSAS: Virtual Dimensions
  34. MSAS: Introducing Member Properties
  35. MSAS: Creating Custom Rollups
  36. MSAS: Creating a Time Dimension
  37. MSAS: Understanding Hierarchies
  38. MSAS: Dimension Storage Modes and Levels
  39. MSAS: Working with Levels and Hierarchies
  40. MSAS: Working with Parent-Child Dimensions
  41. MSAS : Basics of Levels
  42. MSAS : Working with Standard Dimensions
  43. MSAS : Shared vs Private Dimensions
  44. Understanding Dimension Basics
  45. MSAS : Office 2000 OLAP Components
  46. MSAS : Client Architecture
  47. MSAS : Cube Storage options
  48. MSAS : Meta data Repository
  49. MSAS : Analysis services Tools for Extended Functionality
  50. MSAS : The Wizards
  51. MSAS : The Analysis Manager and Analysis Server
  52. MSAS : The Data warehousing framework of SQL Server 2000 - Part 2
  53. MSAS : The Data warehousing framework of SQL Server 2000 - Part 1
  54. MSAS : Microsoft Data Warehousing Overview
  55. MSAS : Browsing the Cube
  56. MSAS : Designing Storage and Processing the Cube
  57. MSAS : Building the Cube Part #3
  58. MSAS : Building the Cube Part #2
  59. MSAS : Building the Cube Part #1
  60. MSAS : Setting up the Database in Analysis Server
  61. MSAS : Preparing to Create the Cube
  62. MSAS : Introducing Analysis Manager Wizards
  63. Microsoft Analysis Services Installation
  64. MSAS - Applying OLAP Cubes
  65. Understanding OLAP Models
  66. Designing the Dimensional Model and Preparing the data for OLAP
  67. Design of the data warehouse: Kimball Vs Inmon
  68. Defining OLAP Solutions and Data Warehouse design
  69. Microsoft Analysis Services Training
  70. Data Warehouse database and OLTP database
  71. Introduction to Data Warehousing

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MSAS : Microsoft Data Warehousing Overview

Author : Exforsys Inc.     Published on: 15th Mar 2005
This tutorial explains various functions available and the tools available for building and managing data warehouses. 

MSAS : Microsoft Data Warehousing Overview

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Modern day enterprises believe that mission critical decisions should be information based. Vast information repositories and historical data available with them need to be analyzed and emerging patterns examined before any decisions are taken. Data warehousing and business intelligence solutions were looked upon as means of achieving this purpose. This need triggered off a proliferation of data warehousing and business intelligence solutions in the market. Initially these solutions were scattered, disparate and focused on specific areas of Data warehousing such as Extraction, Transformation and Load (ETL) solutions at one end and analysis and reporting tools at the other end. OLAP solutions were developed independently and data mining required the deployment of a separate tool. Integration of these various facets of Data warehousing was attempted by large organizations with huge hardware, software and manpower resources to spare. Small and medium enterprises found it impossible to even consider the possibilities of data warehousing in the face of the huge outlays required. It was in this environment that Microsoft entered the market with its integrated data warehousing and business intelligence package –the SQL Server 7.0.

Significantly Microsoft attempted to create an environment for data warehousing called Microsoft data warehousing framework which includes the following functions.

1. Providing access to data from a variety of sources.
2. Building data warehouses and data marts.
3. Transforming data and populating data warehouses and data marts.
4. Creating cubes and storing cubes for client applications
5. Providing access to OLAP cubes for client applications.
6. Providing tools to manage the data warehouse
7. Storing and providing access to meta data.

The SQL Servers released by Microsoft provide for the following tools for building and managing data warehouses.

1. OLE DB for access to data from a variety of sources
2. The Enterprise manager for building data warehouses and data marts
3. DTS for transforming data and populating data warehouses and data marts
4. OLAP services for creating cubes and storing cube data in a relational or multidimensional format.
5. The PivotTable services for client access to cube data
6. English query for natural language access to data .
7. The SQL server database management tools.
8. The repository for storing metadata.

Apart from this Microsoft created what is known as the “Microsoft Data Warehousing alliance”. This is a group of companies, who have joined with Microsoft in supporting this framework for data warehousing. These companies provide tools that work with the Microsoft data warehousing framework. The Alliance partners are many, but a few names are listed under to give an idea of the kind of tools the alliance is developing.

• AppsCo’s Software Limited has developed a tool called AppMart which automates the process of creating data marts.
• DWSoft has created a software called DWGuide. This tool provides a user friendly way to access Microsoft repository.
• Data Junction has created some of the most widely used data transformation tools. These include Data Junction, Cambiro, DJEngine, Custom database Interface SDK, Streaming Data SDK and Data Junction Extraction Language(DJXL).
• Informatica has produced a set of data transformation tools for rapid development. These include PowerCenter, Powermart, Business Components, Power Connect and PowerPlugs.

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Microsoft’s SQL Server 2000 is described as the “most comprehensive offering to the data warehousing ecosystem. Microsoft leverages all SQL server based technologies to deliver a comprehensive business intelligence platform with advance data warehousing techniques, analytic functionality, excellent performance and scalability across platforms. The server includes a high performance relational database, OLAP tools, data mining tools, Data transformation services including ETL tools, Meta data services and the English Query. In marketing this server Microsoft focused upon pushing business intelligence to the edge of the enterprise, making it pervasive and more reachable to all levels and types of users. The entire package was dealt out with the well proven Microsoft strategy of fast implementation, ease of learning and use, low cost and high value and fast return on investment.

The entire technology of the SQL Server 2000 was built on in-house technological strengths. Partnerships were used to create a large base of specialized business intelligence tools and applications to support the platform. They were harnessed to simplify and accelerate the adoption of the technology and also make the resources easily accessible to a large number of users.

The Microsoft Data warehousing platform is built on SQL Server technology which was the backbone of the .NET servers. It maintained total control on design, development and deployment of the server and provided an array of application interfaces built on flexible and extensible object oriented component model. These interfaces provide access to all Business intelligence resources with the flexibility and control to address any application requirement. The historical problem of scalability of Microsoft servers was overcome by architectural improvements. It also received a boost from the fast SMP hardware that came in around this time. Today, the server offers a range of services that cater to the low, middle and high ends in terms of capacity and scalability.



 
This tutorial is part of a Analysis Services Training tutorial series. Read it from the beginning and learn yourself.

Analysis Services Training

  1. MSAS - Browsing the Dependency Network
  2. MSAS - Building a Relational Decision Tree Model
  3. MSAS - Introduction to Data Mining
  4. MSAS - Applying security to a Dimension
  5. Tutorial 65: MSAS - Managing Cube Roles
  6. MSAS - Understanding Database Roles
  7. MSAS - Securing User Authentication
  8. MSAS - Introducing Analysis Services Security
  9. MSAS - Writebacks
  10. MSAS - Defining and Creating Drillthrough
  11. MSAS - Defining and Creating Auctions
  12. MSAS - Creating and Maintaining Calculated Members in Virtual Cubes
  13. MSAS - Building a Virtual Cube
  14. MSAS - Understanding Virtual Cubes
  15. MSAS - Introducing Solve Order
  16. MSAS - Implementing Calculations Using MDX Part 2
  17. MSAS - Implementing Calculations Using MDX Part 1
  18. MSAS - Merging Partitions
  19. MSAS - Introduction and Managing Partitions
  20. MSAS - Troubleshooting Cube Processing
  21. MSAS - Optimizing Cube Processing
  22. MSAS - Processing Dimensions and Cubes
  23. MSAS - Introducing Dimension and Cube Processing
  24. MSAS: Optimization Tuning Part 2
  25. MSAS: Optimization Tuning Part 1
  26. MSAS: Usage-Based Optimization
  27. MSAS: Analysis Services Aggregations
  28. MSAS: The Storage Design Wizard
  29. MSAS: Analysis Server Cube Storage
  30. MSAS: Defining Cube Properties
  31. MSAS: Introduction and Working with Measures
  32. MSAS: Introduction and Working with Cubes
  33. MSAS: Virtual Dimensions
  34. MSAS: Introducing Member Properties
  35. MSAS: Creating Custom Rollups
  36. MSAS: Creating a Time Dimension
  37. MSAS: Understanding Hierarchies
  38. MSAS: Dimension Storage Modes and Levels
  39. MSAS: Working with Levels and Hierarchies
  40. MSAS: Working with Parent-Child Dimensions
  41. MSAS : Basics of Levels
  42. MSAS : Working with Standard Dimensions
  43. MSAS : Shared vs Private Dimensions
  44. Understanding Dimension Basics
  45. MSAS : Office 2000 OLAP Components
  46. MSAS : Client Architecture
  47. MSAS : Cube Storage options
  48. MSAS : Meta data Repository
  49. MSAS : Analysis services Tools for Extended Functionality
  50. MSAS : The Wizards
  51. MSAS : The Analysis Manager and Analysis Server
  52. MSAS : The Data warehousing framework of SQL Server 2000 - Part 2
  53. MSAS : The Data warehousing framework of SQL Server 2000 - Part 1
  54. MSAS : Microsoft Data Warehousing Overview
  55. MSAS : Browsing the Cube
  56. MSAS : Designing Storage and Processing the Cube
  57. MSAS : Building the Cube Part #3
  58. MSAS : Building the Cube Part #2
  59. MSAS : Building the Cube Part #1
  60. MSAS : Setting up the Database in Analysis Server
  61. MSAS : Preparing to Create the Cube
  62. MSAS : Introducing Analysis Manager Wizards
  63. Microsoft Analysis Services Installation
  64. MSAS - Applying OLAP Cubes
  65. Understanding OLAP Models
  66. Designing the Dimensional Model and Preparing the data for OLAP
  67. Design of the data warehouse: Kimball Vs Inmon
  68. Defining OLAP Solutions and Data Warehouse design
  69. Microsoft Analysis Services Training
  70. Data Warehouse database and OLTP database
  71. Introduction to Data Warehousing
 

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