Cis 717 2 data warehouse design week 2
Download
1 / 35

CIS 717.2 Data Warehouse Design Week 2 - PowerPoint PPT Presentation


  • 98 Views
  • Uploaded on

CIS *717.2 Data Warehouse Design Week 2 . Dimensional Modeling Primer Data Warehouse Models OLAP Operations. Instructor Carmela R. Balassiano Feb 5, Spring 2007. The Business Dimensional Lifecycle Overview (a.k.a. Our Course Roadmap). Technical Architecture Design.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'CIS 717.2 Data Warehouse Design Week 2' - sybil


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Cis 717 2 data warehouse design week 2 l.jpg

CIS *717.2 Data Warehouse DesignWeek 2

Dimensional Modeling Primer

Data Warehouse Models

OLAP Operations

Instructor

Carmela R. Balassiano

Feb 5, Spring 2007


Slide2 l.jpg

The Business Dimensional Lifecycle

Overview (a.k.a. Our Course Roadmap)

TechnicalArchitectureDesign

ProductSelection &Installation

Business

Requirement

Definition

(week1)

DimensionalModeling

PhysicalDesign

Data StagingDesign &Development

ProjectPlanning

Deployment

MaintenanceandGrowth

End-UserApplicationSpecification

End-UserApplicationDevelopment

Project Management

Color Legend: done; In Progress; TBD


What is a data warehouse what is data warehousing l.jpg
What is a Data Warehouse?What is Data Warehousing?

Common definitions of a Data Warehouse

  • A decision support database that is maintained separately from the organization’s operational database

    • Support information processing by providing a solid platform of consolidated, historical data for analysis.

  • “A data warehouse is asubject-oriented, integrated, time-variant, and nonvolatilecollection of data in support of management’s decision-making process.”—W. H. Inmon

  • Data warehousing:

    • The process of constructing and using data warehouses


Data warehouse subject oriented l.jpg
Data Warehouse—Subject-Oriented

  • Organized around major subjects, such as customer, product, sales

  • Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing

  • Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process


Data warehouse integrated l.jpg
Data Warehouse—Integrated

  • Constructed by integrating multiple, heterogeneous data sources

    • relational databases, flat files, on-line transaction records

  • Data cleaning and data integration techniques are applied.

    • Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources

      • E.g., Hotel price: currency, tax, breakfast covered, etc.

    • When data is moved to the warehouse, it is converted.


Data warehouse time variant l.jpg
Data Warehouse—Time Variant

  • The time horizon for the data warehouse is significantly longer than that of operational systems

    • Operational database: current value data

    • Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)

  • Every key structure in the data warehouse

    • Contains an element of time, explicitly or implicitly

    • But the key of operational data may or may not contain “time element”


Data warehouse nonvolatile l.jpg
Data Warehouse—Nonvolatile

  • A physically separate store of data transformed from the operational environment

  • Operational update of data does not occur in the data warehouse environment

    • Does not require transaction processing, recovery, and concurrency control mechanisms

    • Requires only two operations in data accessing:

      • initial loading of data and access of data


Data warehouse vs heterogeneous dbms l.jpg
Data Warehouse vs. Heterogeneous DBMS

  • Traditional heterogeneous DB integration: A query driven approach

    • Build wrappers/mediators on top of heterogeneous databases

    • When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set

    • Complex information filtering, compete for resources

  • Data warehouse: update-driven, high performance

    • Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis


Data warehouse vs operational dbms l.jpg
Data Warehouse vs. Operational DBMS

  • OLTP (on-line transaction processing)

    • Major task of traditional relational DBMS

    • Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.

  • OLAP (on-line analytical processing)

    • Major task of data warehouse system

    • Data analysis and decision making



Why separate data warehouse l.jpg
Why Separate Data Warehouse?

  • High performance for both systems

    • DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery

    • Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation

  • Different functions and different data:

    • missing data: Decision support requires historical data which operational DBs do not typically maintain

    • data consolidation: DW requires consolidation (aggregation, summarization) of data from heterogeneous sources

    • data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

  • Note: There are more and more systems which perform OLAP analysis directly on relational databases


What is a dimensional model l.jpg
What is a Dimensional Model?

A dimensional model is a star schema that contains two types of tables, fact tables and dimensions tables.

  • Fact table (quantitative) – a fact table is the primary table in a dimensional model where the numerical performance measurement of the business are stored. I.e. attributes of numeric and additive. Example: quantity sold, dollar sales amount.

  • Dimension table ( descriptive) – tables that contain the textual descriptors of the business. Example: product and brand descriptions.


Dimensional modeling primer the design process l.jpg
Dimensional Modeling Primer – the design process

Transform business requirements document into a 4 step design process

  • Decide what Business process to model

  • Decide what level of data detail (grain) – a.k.a. level of granularity of the fact table be made available .

  • Identify the required Dimensions

  • Decide what goes into the fact table(s)

  • And keep it simple!


Design of data warehouse a business analysis framework l.jpg
Design of Data Warehouse: A Business Analysis Framework

  • Four views regarding the design of a data warehouse

    • Top-down view

      • allows selection of the relevant information necessary for the data warehouse

    • Data source view

      • exposes the information being captured, stored, and managed by operational systems

    • Data warehouse view

      • consists of fact tables and dimension tables

    • Business query view

      • sees the perspectives of data in the warehouse from the view of end-user


Tips from the trenches understanding facts and dimensions l.jpg
Tips from the Trenches Understanding FACTS and DIMENSIONS

  • Think of how an end user or analysts looks at the business

    • A salesperson analyses revenue by customer, product, market and time period

    • A financial analyst tracks actuals and budgets byline item, product and time period

    • A marketing person reviews shipments by product, market and time period.


Slide16 l.jpg

The facts:

What is being analyzed or reviewed in each case are: revenues, actuals, budget and shipments. These items belong to the fact tables

The dimensions:

The business dimensions the “by” items– are product, market, time period and line item: these items belong in the dimension tables.

Tips from the Trenches Understanding FACTS and DIMENSIONS (cont.)Copied from Building a data warehouse by Vidette Poe, Prentice Hall , 1996 page 123

You are analyzing facts by, or through, different dimensions.


Example of star schema l.jpg

product

timePeriod

item_key

item_name

brand

type

supplier_type

time_key

day

day_of_the_week

month

quarter

year

market

branch

location_key

street

city

state_or_province

country

branch_key

branch_name

branch_type

Example of Star Schema

Sales Fact Table

timePeriod _key

product_key

branch_key

market_key

units_sold

dollars_sold

avg_sales

Measures


Dimensional modeling primer l.jpg
Dimensional Modeling Primer

  • Modeling data warehouses: dimensions & measures

    • Star schema: A fact table in the middle connected to a set of dimension tables

    • Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake

    • Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation


Example of fact constellation l.jpg

item

Shipping Fact Table

time

item_key

item_name

brand

type

supplier_type

time_key

Sales Fact Table

time_key

day

day_of_the_week

month

quarter

year

item_key

time_key

shipper_key

item_key

from_location

location

branch_key

location_key

street

city

province_or_state

country

shipper

branch

to_location

location_key

shipper_key

shipper_name

location_key

shipper_type

branch_key

branch_name

branch_type

dollars_cost

units_sold

units_shipped

dollars_sold

avg_sales

Measures

Example of Fact Constellation


Example of snowflake schema l.jpg

item

time

item_key

item_name

brand

type

supplier_key

time_key

day

day_of_the_week

month

quarter

year

city

location

branch

location_key

street

city_key

city_key

city

state_or_province

country

branch_key

branch_name

branch_type

Example of Snowflake Schema

supplier

Sales Fact Table

time_key

supplier_key

supplier_type

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures


Multidimensional data l.jpg
Multidimensional Data

Hierarchical summarization paths Dimensions:

Product, Location, Time

Sales volume as a function of product, month, and region

Region

Industry Region Year

Category Country Quarter

Product City Month Week

Office Day

Product

Month


A sample data cube l.jpg

Total annual sales

of TV in U.S.A.

Date

2Qtr

1Qtr

sum

3Qtr

4Qtr

Product

TV

U.S.A

PC

VCR

sum

Canada

Country

Mexico

All, All, All

sum

A Sample Data Cube


Typical olap operations l.jpg
Typical OLAP Operations

  • Roll up (drill-up): summarize data

    • by climbing up hierarchy or by dimension reduction

  • Drill down (roll down): reverse of roll-up

    • from higher level summary to lower level summary or detailed data, or introducing new dimensions

  • Slice and dice:project and select

  • Pivot (rotate):

    • reorient the cube, visualization, 3D to series of 2D planes

  • Other operations

    • drill across: involving (across) more than one fact table

    • drill through: through the bottom level of the cube to its back-end relational tables (using SQL)


Oracle rollup cube and grouping sets version 10 2 sample queries group by cube clause l.jpg
Oracle Rollup, Cube, and Grouping Sets Version 10.2 sample queries- GROUP BY CUBE() clause

SELECT ch.channel_desc, calendar_month_desc, co.country_name, TO_CHAR(SUM(s.amount_sold), '9,999,999,999') SALES$ FROM sales s, customers cu, times t, channels ch, countries co WHERE s.time_id = t.time_idAND s.cust_id = cu.cust_idAND s.channel_id = ch.channel_idAND ch.channel_desc IN ('Direct Sales', 'Internet')AND t.calendar_month_desc IN ('2000-09', '2000-10')AND co.country_name LIKE 'U%'GROUP BY CUBE (channel_desc, t.calendar_month_desc, co.country_name);


Oracle rollup cube and grouping sets version 10 2 sample queries rollup clause l.jpg
Oracle Rollup, Cube, and Grouping Sets Version 10.2 sample queries- ROLLUP clause

SELECT ch.channel_desc, t.calendar_month_desc, co.country_name,TO_CHAR(SUM(s.amount_sold), '9,999,999,999') SALES$FROM sales s, customers cu, times t, channels ch, countries coWHERE s.time_id = t.time_id AND s.cust_id = cu.cust_idAND s.channel_id = ch.channel_idAND cu.country_id = co.country_idAND ch.channel_desc IN ('Direct Sales','Internet') AND t.calendar_month_desc IN ('2000-09', '2000-10')AND co.country_name LIKE 'U%'GROUP BY ROLLUP(ch.channel_desc, t.calendar_month_desc, co.country_name);


Three data warehouse models l.jpg
Three Data Warehouse Models

  • Enterprise warehouse

    • collects all of the information about subjects spanning the entire organization

  • Data Mart

    • a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart

      • Independent vs. dependent (directly from warehouse) data mart

  • Virtual warehouse

    • A set of views over operational databases

    • Only some of the possible summary views may be materialized


Data warehouse design process summary l.jpg
Data Warehouse Design Process Summary

  • Top-down, bottom-up approaches or a combination of both

    • Top-down: Starts with overall design and planning (mature)

    • Bottom-up: Starts with experiments and prototypes (rapid)

  • From software engineering point of view

    • Waterfall: structured and systematic analysis at each step before proceeding to the next

    • Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around

  • Typical data warehouse design process

    • Choose a business process to model, e.g., orders, invoices, etc.

    • Choose the grain (atomic level of data) of the business process

    • Choose the dimensions that will apply to each fact table record

    • Choose the measure that will populate each fact table record


Data warehouse design process summary cont l.jpg
Data Warehouse Design Process Summary (cont.)

Please remember!

If the presentation area is based on a relational database, then these dimensionally modeled tables are referred to as star schema. If the presentation area is based on a multidimensional database or OLAP technology then data is stored in cubes.


Data warehouse a multi tiered architecture l.jpg

Monitor

&

Integrator

OLAP Server

Metadata

Analysis

Query

Reports

Data mining

Other

sources

Serve

Data

Warehouse

Extract

Transform

Load

Refresh

Operational

DBs

Data Marts

Data Sources

Data Storage

OLAP Engine

Front-End Tools

Data Warehouse: A Multi-Tiered Architecture


Data warehouse back end tools and utilities l.jpg
Data Warehouse Back-End Tools and Utilities

  • Data extraction

    • get data from multiple, heterogeneous, and external sources

  • Data cleaning

    • detect errors in the data and rectify them when possible

  • Data transformation

    • convert data from legacy or host format to warehouse format

  • Load

    • sort, summarize, consolidate, compute views, check integrity, and build indices and partitions

  • Refresh

    • propagate the updates from the data sources to the warehouse

These utilities will be demonstrated during our scheduled on campus vendor demo with Informatica on 2/15/07


Metadata repository l.jpg
Metadata Repository

  • Meta data is the data defining warehouse objects. It stores:

  • Description of the structure of the data warehouse

    • schema, view, dimensions, hierarchies, derived data defn, data mart locations and contents

  • Operational meta-data

    • data lineage (history of migrated data and transformation path), currency of data (active, archived, or purged), monitoring information (warehouse usage statistics, error reports, audit trails)

  • The algorithms used for summarization

  • The mapping from operational environment to the data warehouse

  • Data related to system performance

    • warehouse schema, view and derived data definitions

  • Business data

    • business terms and definitions, ownership of data, charging policies


Olap server architectures l.jpg
OLAP Server Architectures

  • Relational OLAP (ROLAP)

    • Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware

    • Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services

    • Greater scalability

  • Multidimensional OLAP (MOLAP)

    • Sparse array-based multidimensional storage engine

    • Fast indexing to pre-computed summarized data

  • Hybrid OLAP (HOLAP)(e.g., Microsoft SQLServer)

    • Flexibility, e.g., low level: relational, high-level: array

  • Specialized SQL servers (e.g., Redbricks)

    • Specialized support for SQL queries over star/snowflake schemas


Data warehouse and olap technology l.jpg
Data Warehouse and OLAP Technology

  • What is a data warehouse? Facts, dimensions, measures

  • A multi-dimensional model of a data warehouse

    • Star schema, snowflake schema, fact constellations

    • A data cube consists of dimensions & measures

  • Data warehouse architecture

  • OLAP operations: drilling, rolling, slicing, dicing and pivoting

  • OLAP servers: ROLAP, MOLAP, HOLAP

  • Efficient computation of data cubes

    • Partial vs. full vs. no materialization

    • Indexing OALP data: Bitmap index and join index

    • OLAP query processing


Bibliography l.jpg
Bibliography

  • Kimball chapter 1,2

  • Oracle web site


Data mining concepts and techniques 2ed 2006 l.jpg
Data Mining: Concepts and Techniques, 2ed. 2006

  • Chapter 3: Data Warehousing and OLAP Technology: An Overview


ad