OLAP IN THE DATA WAREHOUSE

1
Chapter 8
Data Warehouse Fundamentals

• OLAP IN THE DATA WAREHOUSE

Paul Chen
2
Summary of Topics

• 1. OLAP Definition, Key features and Benefits
  
• 2. How OLAP differs from OATP?
• 3. Multidimensional data ? What? Why? How To
  Use?
• 4. OLAP Server Query, Features and
  Applications.
• 5. Category of OLAP tools -Multi-Dimensional
  OLAP (MOLAP), Relational OLAP (ROLAP), and
  Managed Query Environment (MQE).
• 6. OLAP extensions to SQL.
• 7. The Microsoft Data Warehousing Framework.

3
Data Warehousing and End-User Access Tools

• Accompanying growth in data warehouses is
  increasing demands for more powerful access tools
  providing advanced analytical capabilities.
• Key developments include
• Online analytical processing (OLAP)
• SQL extensions for complex data analysis
• Data mining tools.

4
Limitations of Other Analysis Methods

• SQL has been the accepted interface for
  retrieving and manipulating data from relational
  databases. These methods are used in OLTP systems
  and in data warehousing environments (referring
  to the environments with simple queries and
  routine reports).
• Now consider information retrieval and
  manipulation in these environments- reports
  writers and spreadsheets.
• Report writers two features- the ability to
  point and click for generating and issuing SQL
  calls, and the capability to format the output
  reports. However, report writers do not support
  multidimensionality. With basic report writers,
  you cannot drill down to lower levels in the
  dimensions. You cannot rotate the results by
  switching rows and columns. The report writers do
  not provide aggregate navigation. Once the report
  is formatted and run, you cannot alter the
  presentation of the result data sets.
  Spreadsheets are still cumbersome for showing all
  the aggregate levels and multidimensional views,
  let alone doing calculations for roll-up and
  drill-down.

5
Topic 1 OLAP Definition

• On-Line Analytical Processing (OLAP) is a
  category of software technology that enables
  analysts, managers and executives to gain insight
  into data through fast, consistent, interactive
  access to a wide variety of possible views of
  information that has been transformed from raw
  data to reflect the real dimensionality of the
  enterprise as understood by the user
• -- DBMS Magazine, April, 1995

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Key Features of OLAP

• Supports analysis, dynamic synthesis and
  consolidation of large volumes of
  multi-dimensional data. Types of analysis ranges
  from basic navigation and browsing (slicing and
  dicing) to calculations, to more complex analyses
  such as time series and complex modeling.
• Is able to drill down or roll up with each
  dimension.
• Is capable of applying mathematical formulas and
  calculations to measures.

7
Key Features of OLAP

• Can easily answer who? and what?.
• Ability to answer what if? and why? type
  questions.
• Distinguishes OLAP from general-purpose query
• tools.
• Enables users to gain a deeper understanding and
  knowledge about various aspects of their
  corporate data through fast, consistent,
  interactive access to a wide variety of possible
  views of the data.
• Can be implemented on the web.

8
OLAP Benefits

• Increased productivity of end-users.
• Reduced backlog of applications development for
  IT staff.
• Retention of organizational control over the
  integrity of corporate data.
• Reduced query drag and network traffic on OLTP
  systems or on the data warehouse.
• Improved potential revenue and profitability.

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Topic 2 OLAP VS OLTP (ON-LINE TRANSACTION
PROCESSING)

• OLTP (RELATIONAL)
• ATOMIZED
• PRESENT
• RECORD-AT-A-TIME
• PROCESS ORIENTED

• OLAP (MULTIDIMENSIONAL)
• SUMMARIZED
• HISTORICAL
• MANY RECORDS-AT-A-TIME
• SUBJECT ORIENTED

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OLAP VS OLTP

• WHILE OLTP APPLICATIONS GENERALLY DO NOT
  REQUIRE HISTORICAL DATA, NEARLY EVERY OLAP
  APPLICATION IS CONCERNED WITH VIEWING TRENDS AND
  THEREFORE REQUIRES HISTORICAL DATA. OLTP
  APPLICATIONS AND DATABASE TEND TO BE ORGANIZED
  AROUND SPECIFIC PROCESSES (SUCH AS ORDER ENTRY),
  OLAP APPLICATIONS TEND TO BE SUBJECT-ORIENTED
  ANSWERING SUCH QUESTIONS AS WHAT PRODUCTS ARE
  SELLING WELL OR WHAT ARE MY WEAKEST SALES
  OFFICES?

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Topic 3 Multi-dimensional Database

• In a multidimensional database, data is stored as
  Facts and Dimensions instead of rows and columns
• Multi-dimensional structures are best visualized
  as cubes of data, and cubes within cubes of data.
  Each side of a cube is a dimension.

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Sample Star Schema
Time
Fact Table
Sales
Time key Date Month quarter year
Store
Product Key Store Key Time Key Fixed
Cost Variable cost Profit margin YTD_Sales_dollars
_by_store YTD_Sales_dollar_by_category YTD_Sales_B
y_department
Month
Product
Product
Store
Product Key
Store key Store name region
Product Name
Category
Product line
Department
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Kinds of Queries

• Display the total sales of all products for past
  five years in all stores.
• Compare total sales for all stores, product by
  product, between years 2000 and 1999
• Show comparison of total sales for all stores,
  product by product, between years 2000 and 1999
  only for those products for reduced sales.

14
Cube

• Users often view and analyze data
  multidimensionality, using hierarchical
  segmentation along each dimension. Thus a user
  may analyze sales along the time dimension (such
  as months within quarters with years), along
  geographical dimension (cities with regions
  within countries), along the organizational
  dimension (sales persons within branches within
  territories). We can conceptualize the approach
  as a cube.

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Cube
Fact Table View
Multi-Dimensional Cube
Property sale
p
Branch
c1
c2
c3
sale
week
price
p2
2
week 2
p1 p2 p3 p4
1 2 2 1
1 2 4 3
c1 c2 c3 c1
4
p3
C3
C1
C2
week 1
1
P1 P4
3
P property
Cells roughly equivalent to records in a
relational database.

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WHAT IS MULTIDIMENSIONAL DATA?

• RELATIONAL DATABASES ARE ORGANIZED AROUND A
  LIST OF RECORDS. EACH RECORD CONTAINS RELATED
  INFORMATION WHICH ARE ORGANIZED INTO FIELDS.
• CUS NAME CUSTOMER TELEPHONE ADDRESS
• JACK 10001 345-4444 40
  MAIN
• WALTER 10002 345-6666 30
  ELM
• HOOVER 10003 345-8588 6
  BELLRED
• THIS TABLE HAS ONLY ONE DIMENSION.

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WHAT IS MULTIDIMENSIONAL DATA?

• LOOKING AT CUSTOMER BY TELEPHONE OR
• TELEPHONE BY CUSTOMER ONLY PRODUCES
• A ONE-FOR-ONE CORRESPONDENCE.

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WHAT IS MULTIDIMENSIONAL DATA?

• Lets take a look at an example of a relational
  table where
• there is more than one-to-one correspondence
  between
• the fields.
• In the following table, we have sales data for
  each product
• In each region-- four products sold in three
  regions. The
• Sales data is a two-dimensional matrix (Product
  and
• Region).

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product/region/sales table
Product Region Sales Nuts
East 50 Nuts
West 40 Nuts
Central 30 Screws East
60 Screws West
50 Screws Central 60
Bolts East 100
Bolts West 120 Bolts
Central 80 Washers
East 90 Washers
West 100 Washers Central
40
20
A Much Better Way To Represent the Data
Two-Dimensional Matrix

• PRODUCT EAST WEST CENTRAL
• NUTS 50 40 30
  
• SCREWS 60 50 60
• BOLTS 100 120 80
• WASHERS 90 100 40

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QUERY ON MULTIDIMENSIONAL DATA

• QUESTIONS LIKE WHAT WERE TOTAL SALES OF
  NUTS? OR WHAT WERE TOTAL SALES FOR THE EAST? TO
  FIND THE ANSWER IN THE TWO DIMENSIONAL TABLE,
  JUST FIND THE CELL CALLED EAST AND ADD UP ALL
  THE NUMBERS IN THE COLUMN.

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AGGREGATION THE KEY TO CONSISTENTLY FAST RESPONSE

• PRODUCT EAST WEST CENTRAL TOTAL
• NUTS 50 40 30
  120
• SCREWS 60 50 60 170
• BOLTS 100 120 80
  300
• WASHERS 90 100 40 230
• TOTAL 300 310
  210 820

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Multiple Reads Database Writes

• In the above example, computing the totals
  involves 28 (44 34) database reads and eight
  database writes. A typical relational database
  can read about 200 records per second and writes
  perhaps 20 records per second. So consolidating
  this tiny database would take less than one
  second. However, for some larger tables,
  computing for totals could take days or even
  weeks to consolidate.

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Relational Database vs. Multidimensional OLAP
Server

• With a multidimensional OLAP server, we can
  perform the same consolidation with row and
  column arithmetic. Whereas a relational database
  can access a few hundred records per second, a
  good OLP server should be capable of
  consolidating 20,000 to 30,000 cells (equivalent
  to records in the relational table) per second,
  including the time to write the totals to the
  database. The multidimensional OLAP database will
  take up less space since the names of the regions
  and products are not repeated in the
  multidimensional database as they are in the
  relational database.

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Multiple Hierarchies And Classes Within Dimensions

• The single biggest factor in determining how many
  dimensions for a database is the existence of
  multiple hierarchies and classes within
  dimensions.
• Classes are typically attributes such as size,
  color and other characteristics that define a
  subset of the members of a dimension.
• For example, a database of shampoo sales might
  want to roll up product sales by size (6 oz, 15
  oz), by type(dry hair, oily hair) and possibly by
  other attributes such as scented/unscented, brand
  name, and so on.

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TERMINOLOGY

• Dimension roughly equivalent to Fields in a
  relational database.
• In the multidimensional data, Product and
  Region are both dimensions.
• Cells roughly equivalent to Records in a
  relational
• database.

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Simple Hierarchies (Roll up) Within Dimensions

• The preceding table can be represented
  graphically as follows

Region Total
Central
East
West
Product Total
Bolts
Washers
nuts
Screws
Individual products roll up into a Product Total
28
Multiple Levels of Hierarchies
29
Slice and Dice

• A three-dimensional array has a total of six
  faces, or views. A four-dimensional array has
  twelve views. An n-dimensional array has n(n-1)
  views. The ability to rotate the data cube is the
  main technology for multi-dimensional reporting
  and is sometimes called slice and dice.

Region
Product
Actual/Forecast
30
Practical Limitations on Database Size

• In general, as the number of dimensions
  increases, the number of cells in the database
  increases exponentially.
• for ex., a two-dimensional database with 100
  products and 100 regions would have 10,000 cells.
  If we add a third dimension for time with 52
  weeks, we now have 520,000 cells.
• Most commercial OLAP servers hit the cell
  limit long before they run out of dimensions.

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Topic 4 A list of some important features
supported by some OLAP Servers

• Special time-series data types
• Special dimensions for variables (complex
  mathematical relationships, such as computed
  averages, and simultaneous equations)
• Multiple hierarchies within a dimension
• Classes with a dimension
• Virtual variables (computed on the fly at run
  time, such as gross margin derived from
  revenues and expenses.

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Examples of OLAP applications in various
functional areas
33
OLAP Applications

• Although OLAP applications are found in widely
  divergent functional areas, all have following
  key features
• multi-dimensional views of data
• support for complex calculations
• time intelligence.

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OLAP Applications - multi-dimensional views of
data

• Core requirement of building a realistic
  business model.
• Provides basis for analytical processing through
  flexible access to corporate data.
• The underlying database design that provides the
  multi-dimensional view of data should treat all
  dimensions equally.

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OLAP Applications - support for complex
calculations

• Must provide a range of powerful computational
  methods such as that required by sales
  forecasting, which uses trend algorithms such as
  moving averages and percentage growth.
• Mechanisms for implementing computational methods
  should be clear and non-procedural.

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OLAP Applications time intelligence

• Key feature of almost any analytical application
  as performance is almost always judged over time.
• Time hierarchy is not always used in same manner
  as other hierarchies.
• Concepts such as year-to-date and
  period-over-period comparisons should be easily
  defined.

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Representing Multi-Dimensional Data

• Example of two-dimensional query.
• What is the total revenue generated by property
  sales in each city, in each quarter of 1997?
• Choice of representation is based on types of
  queries end-user may ask.
• Compare representation - three-field relational
  table versus two-dimensional matrix.

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Multi-dimensional Data as Three-field table
versus Two-dimensional Matrix
39
Representing Multi-Dimensional Data

• Example of three-dimensional query.
• What is the total revenue generated by property
  sales for each type of property (Flat or House)
  in each city, in each quarter of 1997?
• Compare representation - four-field relational
  table versus three-dimensional cube.

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Multi-dimensional Data as Four-field Table versus
Three-dimensional Cube
41
Representing Multi-Dimensional Data

• Cube represents data as cells in an array.
• Relational table only represents
  multi-dimensional data in two dimensions.

42
Multi-Dimensional OLAP Servers

• Use multi-dimensional structures to store data
  and relationships between data.
• Multi-dimensional structures are best visualized
  as cubes of data, and cubes within cubes of data.
  Each side of cube is a dimension.
• A cube can be expanded to include other
  dimensions.

43
Multi-Dimensional OLAP Servers

• A cube supports matrix arithmetic.
• Multi-dimensional query response time depends on
  how many cells have to be added on the fly.
• As number of dimensions increases, number of the
  cubes cells increases exponentially.

44
Multi-Dimensional OLAP Servers

• However, majority of multi-dimensional queries
  use summarized, high-level data.
• Solution is to pre-aggregate (consolidate) all
  logical subtotals and totals along all
  dimensions.
• Pre-aggregation is valuable, as typical
  dimensions are hierarchical in nature.
• (e.g. Time dimension hierarchy - years, quarters,
  months, weeks, and days)

45
Multi-Dimensional OLAP Servers

• Predefined hierarchy allows logical
  pre-aggregation and, conversely, allows for a
  logical drill-down.
• Supports common analytical operations
• Consolidation
• Drill-down
• Slicing and dicing.

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Multi-Dimensional OLAP Servers

• Consolidation - aggregation of data such as
  simple roll-ups or complex expressions
  involving inter-related data.
• Drill-Down - is reverse of consolidation and
  involves displaying the detailed data that
  comprises the consolidated data.
• Slicing and Dicing - (also called pivoting)
  refers to the ability to look at the data from
  different viewpoints.

47
Multi-Dimensional OLAP servers

• Can store data in a compressed form by
  dynamically selecting physical storage
  organizations and compression techniques that
  maximize space utilization.
• Dense data (ie., data that exists for high
  percentage of cells) can be stored separately
  from sparse data (ie., significant percentage of
  cells are empty).

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Multi-Dimensional OLAP Servers

• Ability to omit empty or repetitive cells can
  greatly reduce the size of the cube and the
  amount of processing.
• Allows analysis of exceptionally large amounts of
  data.

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Multi-Dimensional OLAP Servers

• In summary, pre-aggregation, dimensional
  hierarchy, and sparse data management can
  significantly reduce the size of the cube and the
  need to calculate values on-the-fly.
• Removes need for multi-table joins and provides
  quick and direct access to arrays of data, thus
  significantly speeding up execution of
  multi-dimensional queries.

50
Codds Rules for OLAP Systems

• In 1993, E.F. Codd formulated twelve rules as the
  basis for selecting OLAP tools.
• Multi-dimensional conceptual view
• Transparency
• Accessibility
• Consistent reporting performance
• Client-server architecture
• Generic dimensionality

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Codds rules for OLAP Systems

• Dynamic sparse matrix handling
• Multi-user support
• Unrestricted cross-dimensional operations
• Intuitive data manipulation
• Flexible reporting
• Unlimited dimensions and aggregation levels.

52
Codds Rules for OLAP Systems

• There are proposals to re-defined or extended the
  rules. For example, to also include
• Comprehensive database management tools
• Ability to drill down to detail (source record)
  level
• Incremental database refresh
• SQL interface to the existing enterprise
  environment

53
Categories of OLAP Tools

• OLAP tools are categorized according to the
  architecture of the underlying database.
• Three main categories of OLAP tools include
• Multi-dimensional OLAP (MOLAP or MD-OLAP)
• Relational OLAP (ROLAP), also called
  multi-relational OLAP
• Managed query environment (MQE)

54
Topic 5 Multi-Dimensional OLAP (MOLAP)

• Use specialized data structures and
  multi-dimensional Database Management Systems
  (MDDBMSs) to organize, navigate, and analyze
  data.
• Data is typically aggregated and stored according
  to predicted usage to enhance query performance.

55
Multi-Dimensional OLAP (MOLAP)

• Use array technology and efficient storage
  techniques that minimize the disk space
  requirements through sparse data management.
• Provides excellent performance when data is used
  as designed, and the focus is on data for a
  specific decision-support application.

56
Multi-Dimensional OLAP (MOLAP)

• Traditionally, require a tight coupling with the
  application layer and presentation layer.
• Recent trends segregate the OLAP from the data
  structures through the use of published
  application programming interfaces (APIs).

57
Typical Architecture for MOLAP Tools
58
MOLAP Tools - Development Issues

• Underlying data structures are limited in their
  ability to support multiple subject areas and to
  provide access to detailed data.
• Navigation and analysis of data is limited
  because the data is designed according to
  previously determined requirements.

59
MOLAP Tools - Development Issues

• MOLAP products require a different set of skills
  and tools to build and maintain the database,
  thus increasing the cost and complexity of
  support.

60
Relational OLAP (ROLAP)

• Fastest-growing style of OLAP technology.
• Supports RDBMS products using a metadata layer -
  avoids need to create a static multi-dimensional
  data structure - facilitates the creation of
  multiple multi-dimensional views of the
  two-dimensional relation.

61
Relational OLAP (ROLAP)

• To improve performance, some products use SQL
  engines to support complexity of
  multi-dimensional analysis, while others
  recommend, or require, the use of highly
  denormalized database designs such as the star
  schema.

62
Typical Architecture for ROLAP Tools
63
ROLAP Tools - Development Issues

• Middleware to facilitate the development of
  multi-dimensional applications. (Software that
  converts the two-dimensional relation into a
  multi-dimensional structure).
• Development of an option to create persistent,
  multi-dimensional structures with facilities to
  assist in the administration of these structures.

64
Managed Query Environment (MQE)

• Relatively new development.
• Provide limited analysis capability, either
  directly against RDBMS products, or by using an
  intermediate MOLAP server.

65
Managed Query Environment (MQE)

• Deliver selected data directly from DBMS or via a
  MOLAP server to desktop (or local server) in form
  of a datacube, where it is stored, analyzed, and
  maintained locally.
• Promoted as being relatively simple to install
  and administer with reduced cost and maintenance.

66
Typical Architecture for MQE Tools
67
MQE Tools - Development Issues

• Architecture results in significant data
  redundancy and may cause problems for networks
  that support many users.
• Ability of each user to build a custom datacube
  may cause a lack of data consistency among users.
  
• Only a limited amount of data can be efficiently
  maintained.

68
Topic 6 OLAP Extensions to SQL

• SQL promoted as easy-to-learn, nonprocedural,
  free-format, DBMS-independent, and international
  standard.
• However, major disadvantage has been inability to
  represent many of the questions most commonly
  asked by business analysts.
• IBM and Oracle jointly proposed OLAP extensions
  to SQL early in 1999, adopted as an amendment to
  SQL.

69
OLAP Extensions to SQL

• Many database vendors including IBM, Oracle,
  Informix, and Red Brick Systems have already
  implemented portions of specifications in their
  DBMSs.
• Red Brick Systems was first to implement many
  essential OLAP functions (as Red Brick
  Intelligent SQL (RISQL)), albeit in advance of
  the standard.

70
OLAP Extensions to SQL - RISQL

• Designed for business analysts.
• Set of extensions that augments SQL with a
  variety of powerful operations appropriate to
  data analysis and decision-support applications
  such as ranking, moving averages, comparisons,
  market share, this year versus last year.

71
Use of the RISQL CUME Function

• Show the quarterly sales for branch office B003,
  along with the monthly year-to-date figures.
• SELECT quarter, quarterlySales,
  CUME(quarterlySales) AS Year-to-Date
• FROM BranchSales
• WHERE branchNo 'B003'

72
Use of the RISQL MOVINGAVG / MOVINGSUM Function

• Show the first six monthly sales for branch
  office B003 without the effect of seasonality.
• SELECT month, monthlySales,
• MOVINGAVG(monthlySales) AS 3-MonthMovingAvg,
• MOVINGSUM(monthlySales) AS 3-MonthMovingSum
• FROM BranchSales
• WHERE branchNo 'B003'

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Topic 7 The Microsoft Data Warehousing Framework

• The Microsoft Data Warehousing Framework is an
  open architecture that is easily integrated with
  existing systems. The Microsoft SQL Server DTS
  tool is used to import, export, and repair or
  transform data (where it is necessary). The
  Framework contains a rich object-oriented
  programming interface for customized data
  warehousing implementations. There is also a
  user interface, the Microsoft SQL Server Analysis
  Services Manager that can be used to configure
  the data warehouse and to populate or update the
  content in a cube. It can be used to schedule
  tasks, monitor performance, and perform queries
  on the data warehouse.

74
Microsoft Data WarehouseProduct

• Data warehousing software has been included with
  Microsoft SQL Servertrade since the release
  of version 7.0 in 1998.

75
The Cube as a Model for the Data Warehouse

• The cube is an imperfect yet satisfactory name
  for a data warehouse repository. How does a data
  warehouse cube differ from a geometrical cube?
  There are a few important differences. A data
  warehouse cube is defined by any number of
  dimensions (it is not limited to three, and
  sometimes a data-warehousing cube may have fewer
  than three dimensions). Dimensions describe a
  data-warehousing cube just as width, height, and
  depth describe a geometrical cube. Where it is
  appropriate, dimensions can be organized into
  any number of levels.

76
The Cube as a Model for the Data Warehouse

• The relationship between two dimensions can be
  modeled using a grid. Dimensions are like the
  labels of along the axes of the grid. The cells
  are facts. Facts correspond to the cross product
  of each dimension of the cube. The data in the
  cell is a measure. Measures are the whole reason
  for the cube. If the cube is about the number of
  items sold, the measure is a count of the number
  of items sold. To repeat the grid example, the
  measure is the number that you would find in the
  grid cell.

77
Dimensions and Levels

• Levels are used to organize dimensions into
  smaller units where necessary. Levels may also
  contain other levels, depending on how they are
  configured in the cube. For example, consider
  that there is a region dimension. Perhaps this
  grocery store operates in three states and uses
  the state boundaries as territorial boundaries.
  Let's say that the region dimension contains
  three levels California, Oregon, and Washington.
  If the business has additional sub-regions such
  as Seattle, Olympia, Yakima, and Spokane in the
  state of Washington, these levels can be added as
  levels to the Washington region, even if such
  detail is not needed for the California and
  Oregon region. Levels are just a convenient way
  to organize facts for a dimension.

78
Facts and Measures

• A fact is about the combination of the various
  dimensions. Locating a fact is like using a
  coordinate system. Just like a position in a
  mathematical cube such as the origin, which
  might be represented by (x0,y0,z0), a fact
  would be represented by specific combination of
  dimensions such as
• (Productbroccoli, RegionSeattle,
  TimeWednesday) yielding a specific fact about
  broccoli being sold in Seattle on Wednesday.
  Depending on the way that the cube is being
  used, the fact may show a measure of something
  like 580 units sold or perhaps a different
  measure like 860.00 in sales. The meaning of
  the measure depends on how the cube is defined.

79
Aggregations

• The mathematical operations of count and sum are
  an essential part of the reason data warehouses
  are useful. These are aggregations. Once
  dimensions are organized and a cube is being
  processed, the aggregations are calculated.
  Generally, aggregations are calculated
  immediately after the cube is initially
  populated or there is a change to the content of
  the cube.

80
Using a Data Warehouse to Make Decisions

• Consider a grocery store. Let's say that a
  promotion has been running for a few days and
  the grocer needs to decide whether or not to run
  the promotion again. A question that the grocer
  might pose would be something like, "Has more of
  the product been sold during the promotion
  period compared to prior periods?"

81
Using a Data Warehouse to Make Decisions

• A grocery store inventory system may record
  prices, products, sales, and
• promotions in a transactional database using
  normalized structures.
• Inventory systems are optimized for inserting
  and updating records and
• perhaps for simple procedural selections such
  as retrieving the cost of an
• item. It is much less likely that the system is
  organized in such a way that it
• would be just as efficient to produce a report
  that details on a day-by-day
• and product-by-product basis the effectiveness
  of a sale. In fact, there is
• usually a contradiction between systems that
  are designed for transactional
• efficiency and those that are designed for
  efficient queries. This is where a
• data warehouse should be used. The data
  warehouse is a separate
• repository that uses the relevant data from
  existing sources in a structure
• that has been optimized for selection.

82
Using a Data Warehouse to Make Decisions

• In this case, it is easy to use a data warehouse
  to answer the grocer's question. The sum of the
  fact records that measure the number of items
  sold using the cube dimensions of products,
  promotion, and time can produce the needed
  results.

83
Using a Data Warehouse to Make Decisions

• To contrast this technique, the information in
  the other systems may not even be in the same
  database. The inventory data source may not be
  the same data source as the customer data
  source or the employee data source. Even if the
  systems are in the same database, it would still
  be a chore to build a system of queries that
  will combine and aggregate the results in a way
  that will produce the correct answer. In fact,
  this effort of combining data sources and
  aggregating the results is just what the data
  warehousing software does best.

84
Viewing a Slice and the Programming Interface to
A Cube

• Although fashioning a data warehouse in multiple
  dimensions may be a simple design choice, and
  performing queries that produce results that span
  several dimensions may not be a significant
  chore for the processor, the very constitution
  of multi-dimensional output can make it difficult
  to display. Charts, graphs, and tables are
  almost always presented in two dimensions.
  There are some good three-dimensional charting
  tools, but beyond that, the chart becomes more
  of a puzzle than a visual aid. A common
  technique of viewing multi-dimensional output is
  to view the output one two-dimensional "slice"
  of a cube at a time. This is the way that the
  Microsoft SQL Server Analysis Services Tool
  displays output.

85
Using DSO (Decision Support Object)

• Fortunately, output is not restricted to two
  dimensions. Microsoft SQL Server Analysis
  Services provides a programming interface to
  multi-dimensional data warehouse output the DSO
  (Decision Support Object). DSO can be used
  programmatically to access the various
  dimensions.

86
Using MDX

• MDX (multidimensional extensions) is a syntax
  designed for querying multidimensional objects
  and data. For such systems, it is more efficient
  and intuitive to use than SQL, which was designed
  for an entirely different set of objects. The
  grammar of an MDX query has a similar feel to the
  grammar of an SQL query. Observe the MDX query
  below which would select the sales figures for
  the broccoli sold in Seattle on Wednesday that I
  used in the example above

87
Using MDX

• Microsoft SQL Server OLAP Services provides an
  architecture for access to multidimensional data.
  This data is summarized, organized, and stored
  in multidimensional structures for rapid response
  to user queries. Through OLE DB for OLAP, a
  PivotTable Service provides client access to
  this multidimensional online analytical
  processing
• (OLAP) data. For expressing queries to this
  data, OLE DB for OLAP employs a full-fledged,
  highly functional expression syntax
  multidimensional expressions (MDX).
•  

88
Using MDX

• OLE DB for OLAP is a set of Component Object
  Model (COM) interfaces designed to extend OLE DB
  for efficient access to multidimensional data.
  ADO has been extended with new objects,
  collections, and methods that are designed to
  take advantage of OLE DB for OLAP. These
  extensions are collectively known as ADO MD
  (multidimensional) and are designed to provide a
  simple, high-level object model for accessing
  tabular and OLAP data.
•  

89
MDX expression

• SELECT axis specification ON COLUMNS,
• axis specification ON ROWS
• FROM cube_name
• WHERE slicer_specification

90
SELECT

• Measures.Sales ON COLUMNS
• Time.Wednesday ON ROWS
• FROM MySalesCube
• WHERE Region.Washington.Seattle
• AND Product.Vegetable.Broccholi
•  
• The output of this query would be a column
  labeled Sales, a row labeled Wednesday and a
  single grid cell at the intersection with the
  sales figure
• 860.00.

91
SELECT

• The SQL Server Analysis Server Manager has an
  interface that accepts MDX queries.
  Alternatively, MDX queries can be incorporated
  into programs that employ the DSO.

92
The Microsoft Data Warehousing Framework

• There is more information about how to build a
  cube using the Microsoft SQL Server Analysis
  Services Manager at MSDN Online, as well as
  guidelines
• to consider for the design and configuration
  of your data warehouse. See the "How to" article
  at this link
• http//msdn.microsoft.com/library/psdk/sql/aghti
  ntro_2vov.htm.