Principles of Data Warehousing

transcript

Principles ofData Warehousing

Lecturer: Dr. Bo Yuan

E-mail: yuanb@sz.tsinghua.edu.cn

Outline

Data Warehouse

Metadata

Data Marts

Multidimensional Data

A Manager’s Questions …

Who are our lowest orhighest margin customers ?

Who are my customers and what products are they buying?

Which customers are most likely to go to the competition ?

What promotions have the biggest impacton revenue?

What is the most effective distribution channel?

What impact will new products/services have on revenue and margins?

Tourists, Farmers and Explorers

Explorers: Seek out the unknown and previously unsuspected rewards hiding in the detailed data.

Farmers: Harvest informationfrom known access paths.

Tourists: Browse information harvested by farmers.

History & Evolution

60’s: Batch Reports Hard to find and analyze information Inflexible and expensive, reprogram every new request

70’s: Terminal-Based DSS and EIS Still inflexible, not integrated with desktop tools

80’s: Desktop Data Access and Analysis Tools Query tools, Spreadsheets, GUIs Easier to use, but only access operational databases

90’s: Data Warehousing OLAP Engines and Tools

Data Everywhere

I cannot find the data I need. Data are scattered over the network. Many versions

I cannot get the data I need. May need experts to get the data.

I cannot understand the data I found. Poorly documented Domain knowledge

I cannot use the data I found. Quality Transformation

What is a data warehouse?

“A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a way that they can understand and use in a business context.”

What is data warehousing?

Data warehousing: techniques for assembling and managing data from various sources for the purpose of answering business questions and making decisions.

A data warehouse is a collection of data that is used primarily in organizational decision making.

A data warehouse is Subject-oriented Integrated Time-varying Non-volatile

Information

Knowledge

Data Warehouse Architecture

Data Warehouse Engine

Optimized Loader

ExtractionCleansing

AnalyzeQuery

Metadata Repository

RelationalDatabases

LegacyData

Purchased Data

ERPSystems

Data Warehouse is …

Subject-Oriented

The data warehouse is organized around subjects of the enterprise (e.g., customers, products, sales) rather than applications areas (e.g., customer invoicing, stock control, product sales).

This is reflected in the need to store decision-support data instead of application-oriented or operational data.

Integrated

The data warehouse integrates corporate application-oriented data from different sources, which often include inconsistent data.

The integrated data sources must be made consistent to present a unified view of the data to the users.

Data Warehouse is …

Time-Variant

Data warehouses are time variant in the sense that they maintain both historical and (nearly) current data.

Historical information is of high importance to decision makers, who often want to understand trends and relationships between data.

Non-Volatile

After the data are loaded into the data warehouse, there are no changes, inserts, or deletes performed against the historical data.

This is logical because the purpose of a warehouse is to enable you to analyze what has occurred.

Operational Systems

Operational Systems Run the business in real time. Based on up-to-the-second data. Optimized to handle large numbers of simple read/write transactions. Optimized for fast response to predefined transactions. Used by people who deal with customers, products.

Database systems have been used traditionally for OLTP. Online Transaction Processing Clerical data processing tasks Detailed, up to date data Structured repetitive tasks

Examples of Operational Data Customer Files Account Balance, Call Record Point of Sale Data, Production Record

Data Warehousing vs. OLTP

Workload

Data warehouses are designed to accommodate ad hoc queries. A data warehouse should be optimized to perform well for a wide variety of possible query operations.

OLTP systems support only predefined operations and might be specifically tuned or designed to support only these operations.

Data Modifications

A data warehouse is updated on a regular basis by the ETL process (run nightly or weekly) using bulk data modification techniques. The users of a data warehouse do not directly update the data warehouse.

In OLTP systems, users routinely issue individual data modification statements to the database. The OLTP database is always up to date, and reflects the current state of each business transaction.

Schema Design

Data warehouses often use denormalized or partially denormalized schemas (such as a star schema) to optimize query performance.

OLTP systems often use fully normalized schemas to optimize update/insert/delete performance, and to guarantee data consistency.

Typical Operations

A typical data warehouse query scans thousands or millions of rows. For example, "Find the total sales for all customers last month."

A typical OLTP operation accesses only a handful of records. For example, "Retrieve the current order for this customer."

Historical Data

Data warehouses usually store months or years of data to support historical analysis.

OLTP systems usually store data from only a few weeks or months to meet the requirements of the current transaction.

Number of Users

Data Warehouses: hundreds of users. OLTP Systems : tens of thousands users.

Database Size

Data Warehouses: 10GB - 1TB OLTP Systems: 100M - 10GB

In summary …

Data warehousing helps optimize the business.

OLTP systems actually run the business.

Data Marts

A data mart is a subset of an organizational data store, usually oriented to a specific purpose or major data subject, that may be distributed to support business needs.

Departmental Data Warehouse

A data warehouse tends to be a strategic but somewhat unfinished concept; a data mart tends to be tactical and aimed at meeting an immediate need.

The smaller-scale data mart is typically easier to build than the enterprise-wide warehouse; can be quickly implemented; and offers tremendous, fast payback for the users.

The downside comes when several department-focused data marts are implemented with no forethought for a future data warehouse that serves the entire enterprise.

Independent Data Marts

Dependent Data Marts

Data Granularity

Granularity is the extent to which a system is broken down into small parts, either the system itself or its description or observation.

A key factor to consider in the design of data warehouses.

The amount of data to be stored in the data warehouse.

Operational Databases Transaction Oriented Detailed Records Lowest Level of Granularity The details of the phone call made by Tom at 2:40pm yesterday

Data Warehouses Decision Making Summarized Data High Levels of Granularity The number of phone calls made by Tom last month

Data Granularity

High Levels of Granularity Reduce storage costs. Reduce CPU usage. Cannot answer certain queries.

• Did Tom call Mary last week? A tradeoff between the volume and the usage of data.

Dual Levels of Granularity Store summarized data on disks.

• Cover 95% decision making queries.• Data access is cheap and convenient.

Store detailed data on tapes .• Cover 5% decision making queries.• Many records need to be involved to process a query.• Data access is expensive and complicated.

Many levels of granularity may be necessary in practice.

Data Partition

Frequently Accessed

Rarely AccessedSmaller Table & Less I/O

Acct. No Name BalanceDate Opened Interest Rate Address

Acct. No Balance Acct. No Name Date OpenedInterest Rate Address

Data Quality

Data warehouses are based on existing data sources.

Data quality matters!

Creating a data warehouse is not a straightforward process.

Warehouse data are from disparate and questionable sources.

Legacy systems are no longer documented.

Corporate wide standards are not well implemented.

Advanced techniques and tools are needed to do the job.

10 Minutes …

Extract, Transform & Load

Extract, Transform & Load (ETL) The interface between external sources and data warehouses ETL may take around 70% of the total workload. Can be implemented manually in any programming language. Commercial ETL tools are widely available.

Extract To consolidate data from different source systems.

• Flat Files• Relational Databases• Customized Applications• Point of Sale Devices• Web Pages

To locate the sources for each data item in the data warehouse.• Not all data are to be extracted.

Extract, Transform & Load

Transform To apply a series of rules or functions to the extracted data to derive the data

for loading into the end target. Typical Functions

• Formatting• Encoding• Aggregating• Splitting• Deriving• Converting• Integrating

Load To load the extracted, cleaned and validated data into the end target.

• Online vs. Offline Loads• Incremental vs. Full Loads

ETL --- Challenges

Trust Credit Card

Savings Loans

Same data different name

Different data same name

Inconsistentname or data

ETL --- Challenges

appl A - balanceappl B - balappl C - currbalappl D - balcurr

appl A - pipeline - cmappl B - pipeline - inappl C - pipeline - feetappl D - pipeline - yds

appl A - m,fappl B - 1,0appl C - x,yappl D - male, female

Data WarehouseExternal Sources

ETL --- Challenges

Same person, different spellings 吕： LV, LUI, LYU

Multiple ways to denote company name Global Systems, GSPL, Global Pty. LTD.

Use of different names for the same object/concept Holland vs. Netherland

Inconsistent data values Age, Marital Status …

Required fields left blank Missing Values

Invalid product codes collected at point of sale Manual entry leads to mistakes. Different conventions: using “-1” or “99999” to indicate an error

Metadata

Metadata is information about data.

Metadata is used to facilitate the understanding, characteristics, and management usage of data.

Metadata can document data about data attributes & structure.

Metadata may include descriptive information about the context, quality and condition, or characteristics of the data.

Metadata for a Book Title, Author, Subject, ISBN, Number of Pages …

Metadata for a data warehouse The data defining warehouse objects A roadmap telling users what are in there and how to find them Far more sophisticated than a data dictionary

Metadata Repository

Data definition and mapping metadata The meaning of each attribute and where the data come from

Data structure metadata The structure of the tables (the data type of each column, primary/foreign key)

Source system metadata The data structure of all the source systems feeding in the warehouse

ETL process metadata The description of each data flow (source, target, transformation, schedule)

Data quality metadata Data quality rules and where they are applicable for, their risk level and actions

Audit metadata The results of all processes (ETL, security log, indexing) in the warehouse

Usage metadata Records about which reports and cubes are used by who and when

Data Models in Data Warehouses

In OLTP systems, data are stored in 2D matrixes.

Data warehouses are subject-oriented Profits, Sales … Data need to be reorganized to better reflect the subjects.

A data warehouse is based on a multidimensional data model, which views data in the form of a data cube.

A data cube allows data to be modeled and viewed in multiple dimensions.

Fact tables contain measures of interest (such as dollars sold) and keys to each of the related dimension tables.

Dimension tables provide the context of the measures such as item (item name, brand), product, location or time(day, week, month, quarter, year).

From Tables to Data Cubes

ID Product Country Date Sales1 TV US 1Qtr 1002 PC Canada 4Qtr 5003 CAR US 2Qtr 304 PC UK 3Qtr 2005 CAR UK 1Qtr 206 CAR UK 2Qtr 157 TV Canada 4Qtr 80

From Tables to Data Cubes

Total annual salesof TV in U.S.A.Date

ntrysum

sum TV

1Qtr 2Qtr 3Qtr 4Qtr

Canada

Cube: A Lattice of Cuboids

time item location supplier

time,item time,location

time,supplier

item,location

item,supplier

location,supplier

time,item,location

time,item,supplier

time,location,supplier

item,location,supplier

time, item, location, supplier

0-D cuboid

1-D cuboids

2-D cuboids

3-D cuboids

4-D cuboid

Data Warehouse Schemas

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 sharing dimension tables, viewed as a collection

of stars, therefore called galaxy schema or fact constellation

The Star Schema

time_keydayday_of_the_weekmonthquarteryear

location_keystreetcityprovince_or_streetcountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

branch_keybranch_namebranch_type

branch

The Star Schema: An Example

customer custId name address city53 joe 10 main sfo81 fred 12 main sfo

111 sally 80 willow la

product prodId name pricep1 bolt 10p2 nut 5

store storeId cityc1 nycc2 sfoc3 la

sale oderId date custId prodId storeId qty amto100 1/7/97 53 p1 c1 1 12o102 2/7/97 53 p2 c1 2 11105 3/8/97 111 p1 c3 5 50

The Snowflake Schema

location_keystreetcity_key

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_sales

Measures

item_keyitem_namebrandtypesupplier_key

branch

supplier_keysupplier_type

supplier

city_keycityprovince_or_streetcountry

The Galaxy Schema

location_keystreetcityprovince_or_streetcountry

location

Sales Fact Table

time_key

item_key

branch_key

location_key

units_sold

dollars_sold

avg_salesMeasures

item_keyitem_namebrandtypesupplier_type

branch

Shipping Fact Table

time_key

item_key

shipper_key

from_location

to_location

dollars_cost

units_shipped

shipper_keyshipper_namelocation_keyshipper_type

shipper

Concept Hierarchy

Europe North_America

CanadaSpainGermany

Vancouver

M. WindL. Chan

......

... ...

region

office

country

TorontoFrankfurtcity

Location

Set-Grouping Hierarchy

[$0 - $1000]

inexpensive

[$0 - $150]

moderate expensive

View of Hierarchies

Bitmap Index

Index on a particular column.

Each value in the column corresponds to a bit vector.

The length of the bit vector: # of unique records.

Not suitable for high cardinality domains

Cust Region TypeC1 Asia RetailC2 Europe DealerC3 Asia DealerC4 America RetailC5 Europe Dealer

RecID Retail Dealer1 1 02 0 13 0 14 1 05 0 1

RecID Asia Europe America1 1 0 02 0 1 03 1 0 04 0 0 15 0 1 0

Base Table Index on Region Index on Type

Online Analytical Processing

Fast Analysis of Shared Multidimensional Information (FASMI)

Slice and Dice: Project and Select

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

Pivot (rotate): Reorient the cube

Browsing a Data Cube

Slicing and dicing

Product

Sales Channel

Region

Retail Direct Special

Household

Telecomm

Audio IndiaFar East

Europe

The Telecomm Slice

Roll-Up & Drill-Down

Sales Channel Region Country State Location Address Sales Representative

Higher Level ofAggregation

Low-levelDetails

Drill-Dow n

JuiceColaMilk Cream

NYLASF

3/1 3/2 3/3 3/4

onProduct

OLAP Server Architectures

Relational OLAP (ROLAP) Use relational DBMS to store and manage warehouse data. ROLAP tools access the data in a relational database and generate SQL

queries to calculate information at the appropriate level as required. Greater scalability

Multidimensional OLAP (MOLAP) Fast query performance due to optimized storage and indexing Automated computation of higher level aggregates of the data Very compact for low dimension data sets. Array model provides natural indexing

Hybrid OLAP (HOLAP) User flexibility Low level: relational High-level: array

Warehouse Products

Computer Associates -- CA-Ingres

Hewlett-Packard -- Allbase/SQL

Informix -- Informix, Informix XPS

Microsoft -- SQL Server

Oracle -- Oracle 7, Oracle Parallel Server

Red Brick -- Red Brick Warehouse

SAS Institute -- SAS

Software AG -- ADABAS

Sybase -- SQL Server, IQ, MPP

Data Warehouse Vendors

Review

What is a data warehouse?

What is data warehousing?

What is the difference between OLTP and data warehousing?

What does ETL stand for?

What is the meaning of Metadata?

What is the star schema?

What is the snowflake schema?

What is an OLAP cube?

What are the most common OLAP operations?

Next Week’s Class Talk

Volunteers are required for next week’s class talk.

Topic: Business Intelligence

Length: 20 minutes plus question time

Suggested Points of Interest Aim & Scope

• Techniques involved

Market• Vendors & Products

Typical applications• Supermarkets, Airlines, Financial Institutes …

Prospect of employment• Major BI companies

The future of BI• Development trends

Project Option--- Data Warehousing

Aim To gain hand-on experiences on data warehousing. To get familiar with popular data warehousing software. To build up teamwork and interpersonal skills.

Deliverables Reports Oral Presentation or Poster

Due Reports must be submitted before Week 14. Oral presentations and posters are scheduled on Week 15.

Software PowerOLAP InstantOLAP Pentaho

Principles of Data Warehousing

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