Introduction To Data Warehousing And Business Intelligence

Description
Data Warehousing and Machine Learning
Aalborg University, Denmark

•1
Introduction to Data Warehousing and
Business Intelligence
Slides kindly borrowed from the course
“Data Warehousing and Machine Learning”
Aalborg University, Denmark
Christian S. Jensen
Torben Bach Pedersen
Christian Thomsen
{csj,tbp,chr}@cs.aau.dk
2
Course Structure
• Business intelligence
? Extract knowledge from large amounts of data collected in a
modern enterprise
? Data warehousing, machine learning
• Purpose
? Acquire theoretical background in lectures and literature studies
? Obtain practical experience on (industrial) tools in practical
exercises
Data warehousing: construction
of a database with only data
analysis purpose
Machine learning: find
patterns automatically
in databases
Business
Intelligence (BI)
•2
3
Literature
• Multidimensional Databases and Data
Warehousing, Christian S. Jensen, Torben Bach
Pedersen, Christian Thomsen, Morgan & Claypool
Publishers, 2010
• Data Warehouse Design: Modern Principles and
Methodologies, Golfarelli and Rizzi, McGraw-Hill, 2009
• Advanced Data Warehouse Design: From
Conventional to Spatial and Temporal Applications,
Elzbieta Malinowski, Esteban Zimányi, Springer, 2008
• The Data Warehouse Lifecycle Toolkit, Kimball et al.,
Wiley 1998
• The Data Warehouse Toolkit, 2
nd
Ed., Kimball and
Ross, Wiley, 2002
4
Overview
• Why Business Intelligence?
• Data analysis problems
• Data Warehouse (DW) introduction
• DW topics
? Multidimensional modeling
? ETL
? Performance optimization
•3
5
What is Business Intelligence (BI)?
• From Encyclopedia of Database Systems:
“[BI] refers to a set of tools and techniques that
enable a company to transform its business
data into timely and accurate information for the
decisional process, to be made available to the
right persons in the most suitable form.”
What is Business Intelligence (BI)?
• BI is different from Artificial Intelligence (AI)
? AI systems make decisions for the users
? BI systems help the users make the right decisions, based on
available data
• Combination of technologies
? Data Warehousing (DW)
? On-Line Analytical Processing (OLAP)
? Data Mining (DM)
? ……
6
•4
7
Why is BI Important?
• Worldwide BI revenue in 2005 = US$ 5.7 billion
? 10% growth each year
? A market where players like IBM, Microsoft, Oracle, and SAP
compete and invest
• BI is not only for large enterprises
? Small and medium-sized companies can also benefit from BI
• The financial crisis has increased the focus on BI
? You cannot afford not to use the “gold” in your data
BI and the Web
• The Web makes BI even more useful
? Customers do not appear “physically” in a store; their behaviors
cannot be observed by traditional methods
? A website log is used to capture the behavior of each customer,
e.g., sequence of pages seen by a customer, the products viewed
? Idea: understand your customers using data and BI!
? Utilize website logs, analyze customer behavior in more detail than
before (e.g., what was not bought?)
? Combine web data with traditional customer data
8
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9
Case Study of an Enterprise
• Example of a chain (e.g., fashion stores or car dealers)
? Each store maintains its own customer records and sales records
? Hard to answer questions like: “find the total sales of Product X from
stores in Aalborg”
? The same customer may be viewed as different customers for
different stores; hard to detect duplicate customer information
? Imprecise or missing data in the addresses of some customers
? Purchase records maintained in the operational system for limited
time (e.g., 6 months); then they are deleted or archived
? The same “product” may have different prices, or different discounts
in different stores
• Can you see the problems of using those data for business
analysis?
10
Data Analysis Problems
• The same data found in many different systems
? Example: customer data across different stores and
departments
? The same concept is defined differently
• Heterogeneous sources
? Relational DBMS, On-Line Transaction Processing (OLTP)
? Unstructured data in files (e.g., MS Word)
? Legacy systems
? …
•6
11
Data Analysis Problems (cont’)
• Data is suited for operational systems
? Accounting, billing, etc.
? Do not support analysis across business functions
• Data quality is bad
? Missing data, imprecise data, different use of systems
• Data are “volatile”
? Data deleted in operational systems (6 months)
? Data change over time – no historical information
12
Data Warehousing
• Solution: new analysis environment (DW) where data are
? Subject oriented (versus function oriented)
? Integrated (logically and physically)
? Time variant (data can always be related to time)
? Stable (data not deleted, several versions)
? Supporting management decisions (different organization)
• Data from the operational systems are
? Extracted
? Cleansed
? Transformed
? Aggregated (?)
? Loaded into the DW
• A good DW is a prerequisite for successful BI
•7
13
DW: Purpose and Definition
• DW is a store of information organized in a unified
data model
• Data collected from a number of different sources
? Finance, billing, website logs, personnel, …
• Purpose of a data warehouse (DW): support
decision making
• Easy to perform advanced analysis
? Ad-hoc analysis and reports
? We will cover this soon ……
? Data mining: discovery of hidden patterns and trends
? You will study this in another course
14
DW Architecture – Data as Materialized Views
DB
DB
DB
DB
DB
Appl.
Appl.
Appl.
Trans.
DW
DM
DM
DM
OLAP
Visua-
lization
Appl.
Appl.
Data
mining
(Local)
Data Marts
(Global) Data
Warehouse
Existing databases
and systems (OLTP) New databases
and systems (OLAP)
Analogy: (data) producers warehouse (data) consumers
•8
15
Function vs. Subject Orientation
DB
DB
DB
DB
DB
Appl.
Appl.
Appl.
Trans.
DW
DM
DM
DM
D-Appl.
D-Appl.
Appl.
Appl.
D-Appl.
Function-oriented
systems
Selected
subjects
All subjects,
integrated
Subject-oriented
systems
Sales
Costs
Profit
16
Top-down vs. Bottom-up
DB
DB
DB
DB
DB
Appl.
Appl.
Appl.
Trans.
DW
DM
DM
DM
D-Appl.
D-Appl.
Appl.
Appl.
D-Appl.
Top-down:
1. Design of DW
2. Design of DMs
Bottom-up:
1. Design of DMs
2. Maybe integration
of DMs in DW
3. Maybe no DW
In-between:
1. Design of DW for
DM1
2. Design of DM2 and
integration with DW
3. Design of DM3 and
integration with DW
4. ...
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17
Hard/Infeasible Queries for OLTP
• Why not use the existing databases (OLTP) for
business analysis?
• Business analysis queries
? In the past five years, which product is the most profitable?
? Which public holiday we have the largest sales?
? Which week we have the largest sales?
? Does the sales of dairy products increase over time?
• Difficult to express these queries in SQL
? 3
rd
query: may extract the “week” value using a function
? But the user has to learn many transformation functions …
? 4
th
query: use a “special” table to store IDs of all dairy products,
in advance
? There can be many different dairy products; there can be many
other product types as well …
• The need of multidimensional modeling
18
Multidimensional Modeling
• Example: sales of supermarkets
• Facts and measures
? Each sales record is a fact, and its sales value is a
measure
• Dimensions
? Group correlated attributes into the same dimension ?
easier for analysis tasks
? Each sales record is associated with its values of
Product, Store, Time
Product Type Category Store City County Date Sales
Top Beer Beverage Trøjborg Århus Århus 25 May, 2009 5.75
Product Store Time
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19
Multidimensional Modeling
• How do we model the Time dimension?
? Hierarchies with multiple levels
? Attributes, e.g., holiday, event
• Advantage of this model?
? Easy for query (more about this later)
• Disadvantage?
? Data redundancy (but controlled redundancy is acceptable)
Day
Week
Month
Year
T
tid day day
#
week
#
month
#
year work
day
…
1 January
1st 2009
1 1 1 2009 No …
2 January
2nd 2009
2 1 1 2009 Yes …
… … … … … … …
20
Quick Review: Normalized Database
Product ID Type Category Price
001 Beer Beverage 6.00
002 Rice Cereal 4.00
003 Beer Beverage 7.00
004 Wheat Cereal 5.00
• Normalized database avoids
? Redundant data
? Modification anomalies
• How to get the original table? (join them)
• No redundancy in OLTP, controlled redundancy in OLAP
Product ID TypeID Price
001 013 6.00
002 052 4.00
003 013 7.00
004 067 5.00
TypeID Type CategoryID
013 Beer 042
052 Rice 099
067 Wheat 099
CategoryID Category
042 Beverage
099 Cereal
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21
OLTP vs. OLAP
OLTP OLAP
Target operational needs business analysis
Data small, operational data large, historical data
Model normalized denormalized/
multidimensional
Query language SQL not unified – but MDX
is used by many
Queries small large
Updates frequent and small infrequent and batch
Transactional recovery necessary not necessary
Optimized for update operations query operations
22
OLAP Data Cube
• Data cube
? Useful data analysis tool in DW
? Generalized GROUP BY queries
? Aggregate facts based on chosen
dimensions
? Product, store, time dimensions
? Sales measure of sale facts
• Why data cube?
? Good for visualization (i.e., text
results hard to understand)
? Multidimensional, intuitive
? Support interactive OLAP
operations
• How is it different from a
spreadsheet?
Store Product Time Sales
Aalborg Bread 2000 57
Aalborg Milk 2000 56
Copenhagen Bread 2000 123
… … … …
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23
On-Line Analytical Processing (OLAP)
• On-Line Analytical Processing
? Interactive analysis
? Explorative discovery
? Fast response times required
• OLAP operations/queries
? Aggregation, e.g., SUM
? Starting level, (Year, City)
? Roll Up: Less detail
? Drill Down: More detail
? Slice/Dice: Selection, Year=2000
102
250
All Time
9 10
11 15
24
Advanced Multidimensional Modeling
• Changing dimensions
? Some dimensions are not static. They change over time.
? A store moves to a new location with more space
? The name of a product changes
? A customer moves from Aalborg Øst to Hasseris
? How do we handle these changes?
• Large-scale dimensional modeling
? How do we coordinate the dimensions in different data
cubes and data marts?
Time Customer Product Supplier
Sales + + +
Costs + +
Profit + + + +
Dimensions
Data
marts
Bus architecture
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25
Extract, Transform, Load (ETL)
• “Getting multidimensional data into the DW”
• Problems
1. Data from different sources
2. Data with different formats
3. Handling of missing data and erroneous data
4. Query performance of DW
• ETL
? Extract (for problem #1)
? Transformations / cleansing (for problems #2, #3)
? Load (for problem #4)
• The most time-consuming process in DW development
? 80% of development time spent on ETL
26
Performance Optimization
• The data warehouse contains GBytes or
even TBytes of data!
• OLAP users require fast query response time
? They don’t want to wait for the result for 1 hour!
? Acceptable: answer within 10 seconds
• Idea: precompute some partial result in advance and store it
? At query time, such partial result can be utilized to derive the final
result very fast
tid pid locid sales
1 1 1 10
2 1 1 20
3 2 3 40
… … … …
Sales
1 billion rows
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27
Materialization Example
• Imagine 1 billion sales rows, 1000 products, 100 locations
• CREATE VIEW TotalSales (pid, locid, total) AS
SELECT s.pid, s.locid, SUM(s.sales)
FROM Sales s
GROUP BY s.pid, s.locid
• The materialized view has 100,000 rows
• Wish to answer the query:
? SELECT p.category, SUM(s.sales)
FROM Products p, Sales s WHERE p.pid=s.pid
GROUP BY p.category
• Rewrite the query to use the view:
? SELECT p.category, SUM(t.total)
FROM Products p, TotalSales t
WHERE p.pid=t.pid GROUP BY p.category
? Query becomes 10,000 times faster!
tid pid locid sales
1 1 1 10
2 1 1 20
3 2 3 40
… … … …
Sales
pid locid sales
1 1 30
2 3 40
… … …
VIEW TotalSales
1 billion rows
100,000 rows
28
Data Warehouse Architecture
• Central
• Federated
• Tiered
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29
Central DW Architecture
• All data in one, central DW
• All client queries directly on the
central DW
• Pros
? Simplicity
? Easy to manage
• Cons
? Bad performance due to no
redundancy/workload distribution
Central
DW
Source Source
Clients
30
Federated DW Architecture
• Data stored in separate data marts,
aimed at special departments
• Logical DW (i.e., virtual)
• Data marts contain detail data
• Pros
? Performance due to distribution
• Cons
? More complex
Logical
DW
Source Source
Clients
Finance
mart
Mrktng
mart
Distr.
mart
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31
Tiered Architecture
• Central DW is materialized
• Data is distributed to data marts in
one or more tiers
• Only aggregated data in cube tiers
• Data is aggregated/reduced as it
moves through tiers
• Pros
? Best performance due to
redundancy and distribution
• Cons
? Most complex
? Hard to manage
Central
DW
32
Common DW Issues
• Metadata management
? Need to understand data = metadata needed
? Greater need in OLAP than in OLTP as “raw” data is used
? Need to know about:
? Data definitions, dataflow, transformations, versions, usage, security
• DW project management
? DW projects are large and different from ordinary SW projects
? 12-36 months and US$ 1+ million per project
? Data marts are smaller and “safer” (bottom up approach)
? Reasons for failure
? Lack of proper design methodologies
? High HW+SW cost
? Deployment problems (lack of training)
? Organizational change is hard… (new processes, data ownership,..)
? Ethical issues (security, privacy,…)
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33
Topics not Covered in the Course
• Privacy/security of data during ETL
? Encryption may not work
? During extraction/transformation, someone may need to know
original values in order to check whether ETL performs correctly
• Data Visualization (VIS)
• Decision Analysis (What-if)
• Customer Relationship Management (CRM)
34
Summary
• Why Business Intelligence?
• Data analysis problems
• Data Warehouse (DW) introduction
• DW Topics
? Multidimensional modeling
? ETL
? Performance optimization
• BI provide many advantages to your organization
? A good DW is a prerequisite for BI
1
Multidimensional Databases
2
Overview
• Cubes: Dimensions, Facts, Measures
• OLAP Queries
• Relational Implementation
• Redundancy
2
3
ER Model vs. Multidimensional Model
• Why don’t we use the ER model in data warehousing?
• ER model: a data model for general purposes
? All types of data are “equal”, difficult to identify the data that is
important for business analysis
? No difference between:
? What is important
? What just describes the important
? Normalized databases spread information
? When analyzing data, the information must be integrated again
? Hard to overview a large ER diagram (e.g., over 100
entities/relations for an enterprise)
ER Model vs. Multidimensional Model
• The multidimensional model
? Its only purpose: data analysis
? It is not suitable for OLTP systems
? More built in “meaning”
? What is important
? What describes the important
? What we want to optimize
? Easy for query operations
• Recognized by OLAP/BI tools
? Tools offer powerful query facilities based on MD design
? Example: TARGIT Analysis
4
3
5
The multidimensional model
• Data is divided into:
? Facts
? Dimensions
• Facts are the important entity: a sale
• Facts have measures that can be aggregated: sales price
• Dimensions describe facts
? A sale has the dimensions Product, Store and Time
• Facts “live” in a multidimensional cube (dice)
? Think of an array from programming languages
• Goal for dimensional modeling:
? Surround facts with as much context (dimensions) as possible
? Hint: redundancy may be ok (in well-chosen places)
? But you should not try to model all relationships in the data (unlike
E/R and OO modeling!)
6
Cube Example
2000 2001
Aalborg
Copenhagen
Milk
Bread
123 127
57 45
56 67
211
4
7
Cubes
• A “cube” may have many dimensions!
? More than 3 - the term ”hypercube” is sometimes used
? Theoretically no limit for the number of dimensions
? Typical cubes have 4-12 dimensions
• But only 2-4 dimensions can be viewed at a time
? Dimensionality reduced by queries via projection/aggregation
• A cube consists of cells
? A given combination of dimension values
? A cell can be empty (no data for this combination)
? A sparse cube has few non-empty cells
? A dense cube has many non-empty cells
? Cubes become sparser for many/large dimensions
8
Dimensions
• Dimensions are the core of multidimensional databases
? Other types of databases do not support dimensions
• Dimensions are used for
? Selection of data
? Grouping of data at the right level of detail
• Dimensions consist of dimension values
? Product dimension have values ”milk”, ”cream”, …
? Time dimension have values ”1/1/2001”, ”2/1/2001”,…
• Dimension values may have an ordering
? Used for comparing cube data across values
? Example: ”percent sales increase compared with last month”
? Especially used for Time dimension
5
9
Dimensions
• Dimensions have hierarchies with levels
? Typically 3-5 levels (of detail)
? Dimension values are organized in a tree structure
? Product: Product->Type->Category
? Store: Store->Area->City->County
? Time: Day->Month->Quarter->Year
? Dimensions have a bottom level and a top level (ALL)
• Levels may have attributes
? Simple, non-hierarchical information
? Day has Workday as attribute
• Dimensions should contain much information
? Time dimensions may contain holiday, season, events,…
? Good dimensions have 50-100 or more attributes/levels
10
Dimension Example
Location
City
Country USA Denmark
Berkeley New York Aalborg Copenhagen
T T
Schema Instance
6
11
Facts
• Facts represent the subject of the desired analysis
? The ”important” in the business that should be analyzed
• A fact is identified via its dimension values
? A fact is a non-empty cell
• Generally, a fact should
? Be attached to exactly one dimension value in each dimension
? Only be attached to dimension values in the bottom levels
? Some models do not require this
12
Types of Facts
• Event fact (transaction)
? A fact for every business event (sale)
• ”Fact-less” facts
? A fact per event (customer contact)
? No numerical measures
? An event has happened for a given dimension value combination
• Snapshot fact
? A fact for every dimension combination at given time intervals
? Captures current status (inventory)
• Cumulative snapshot facts
? A fact for every dimension combination at given time intervals
? Captures cumulative status up to now (sales in year to date)
• Every type of facts answers different questions
? Often both event facts and both kinds of snapshot facts exist
7
13
Granularity
• Granularity of facts is important
? What does a single fact mean?
? Level of detail
? Given by combination of bottom levels
? Example: ”total sales per store per day per product”
• Important for number of facts
? Scalability
• Often the granularity is a single business transaction
? Example: sale
? Sometimes the data is aggregated (total sales per store per day
per product)
? Might be necessary due to scalability
• Generally, transaction detail can be handled
? Except perhaps huge clickstreams etc.
14
Measures
• Measures represent the fact property that the users want
to study and optimize
? Example: total sales price
• A measure has two components
? Numerical value: (sales price)
? Aggregation formula (SUM): used for aggregating/combining a
number of measure values into one
? Measure value determined by dimension value combination
? Measure value is meaningful for all aggregation levels
8
15
Types of Measures
• Three types of measures
• Additive
? Can be aggregated over all dimensions
? Example: sales price
? Often occur in event facts
• Semi-additive
? Cannot be aggregated over some dimensions - typically time
? Example: inventory
? Often occur in snapshot facts
• Non-additive
? Cannot be aggregated over any dimensions
? Example: average sales price
? Occur in all types of facts
16
Schema Documentation
• No well-defined standard
• Our own notation
? T level corresponds to ALL
? Record the measures
• You could also use a UML-
like notation
• Modeling and OLAP tools may
have their own notation
Store
County
Store
dimension
Store Product
Category
Product
Product
dimension
Day
Month
Year
Time
Time
dimension
Customer
Customer
dimension
Cust. group
Customer
T T T T
Sales price
Count
Avg. sales price
9
17
Why the schema cannot answer question X
• Possible reasons
? Certain measures not included in fact table
? Granularity of facts too coarse
? Particular dimensions not in DW
? Descriptive attributes missing from dimensions
? Meaning of attributes/measures deviate from the
expectation of data analysts (users)
? ……
18
ROLAP
• Relational OLAP
• Data stored in relational tables
? Star (or snowflake) schemas used for modeling
? SQL used for querying
• Pros
? Leverages investments in relational technology
? Scalable (billions of facts)
? Flexible, designs easier to change
? New, performance enhancing techniques adapted from MOLAP
? Indices, materialized views
• Cons
? Storage use (often 3-4 times MOLAP)
? Response times
Product ID Store ID Sales
1 3 2
2 1 7
3 2 3
… … …
10
19
MOLAP
• Multidimensional OLAP
• Data stored in special multidimensional data structures
? E.g., multidimensional array on hard disk
• Pros
? Less storage use (“foreign keys” not stored)
? Faster query response times
• Cons
? Up till now not so good scalability
? Less flexible, e.g., cube must be re-computed when design changes
? Does not reuse an existing investment (but often bundled with
RDBMS)
? Not as open technology
d
2
\ d
1
1 2 3
1 0 7 0
2 2 0 0
3 0 0 3
MOLAP data cube
20
HOLAP
• Hybrid OLAP
• Detail data stored in relational tables (ROLAP)
• Aggregates stored in multidimensional structures (MOLAP)
• Pros
? Scalable (as ROLAP)
? Fast (as MOLAP)
• Cons
? High complexity
11
21
Relational Implementation
• Goal for dimensional modeling: surround the facts with
as much context (dimensions) as we can
• Granularity of the fact table is important
? What does one fact table row represent?
? Important for the size of the fact table
? Often corresponding to a single business transaction (sale)
? But it can be aggregated (sales per product per day per store)
• Some properties
? Many-to-one relationship from fact to dimension
? Many-to-one relationships from lower to higher levels in the
hierarchies
22
Relational Design
• One completely de-normalized table
? Bad: inflexibility, storage use, bad performance, slow update
• Star schemas
• Snowflake schemas
Product Type Category Store City County Date Sales
Top Beer Beverage Trøjborg Århus Århus 25 May 2009 5.75
Product Store Time
12
23
Star Schema Example

ProductId StoreId TimeId Sale
1 1 1 5.75


ProductID Product Type Category
1 Top Beer Beverage

StoreID Store City County
1 Trøjborg Århus Århus

TimeID Day Month Year
1 25. Maj 1997

• Star schemas
? One fact table
? De-normalized dimension tables
? One column per level/attribute
24
Relational Implementation
• The fact table stores facts
? One column for each measure
? One column for each dimension (foreign key to dimension table)
? Dimensions keys make up composite primary key
• A dimension table stores a dimension
• What are the disadvantages of using production codes as
the key?
? E.g., product dimension, production code: AABC1234
? E.g., customer dimension, CPR number: 020208-1357
• Use surrogate key (“meaningless” integer key), which only
allows the linking between its dimension table and the fact
table
For Extract-Transform-Load, we need to keep a mapping from
production key to surrogate key (more about this in lecture #4)
13
25
Snowflake Schema Example

ProductId StoreId TimeId Sale
1 1 1 5.75


ProductID Product TypeID
1 Top 1

StoreID Store CityID
1 Trøjborg 1

TimeID Day MonthID
1 25. 1

CityID City CountyId
1 Århus 1

TypeID Type CategoryID
1 Beer 1

MonthID Month YearID
1 May 1

• Snowflake schemas
? Dimensions are normalized
? One dimension table per level
? Each dimension table has
integer key, level name, and
one column per attribute
26
• Suppose that we want to replace the original Store hierarchy
A by a new hierarchy B
• How do we modify the star schema to reflect this?
• How do we modify the snowflake schema to reflect this?
Store
City
County
T
Store Schema A Store Schema B
Store
City
County
Country
T
Question Time
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27
Star vs Snowflake
• Star Schemas
+ Simple and easy overview ?ease-of-use
+ Relatively flexible
+ Dimension tables often relatively small
+ “Recognized” by many RDBMSes -> good performance
- Hierarchies are ”hidden” in the columns
- Dimension tables are de-normalized
• Snowflake schemas
+ Hierarchies are made explicit/visible
+ Very flexible
+ Dimension tables use less space
- Harder to use due to many joins
- Worse performance
28
Redundancy in the DW
• Only very little or no redundancy in fact tables
? The same fact data only stored in one fact table
• Redundancy is mostly in dimension tables
? Star dimension tables have redundant entries for the higher levels
• Redundancy problems?
? Inconsistent data – the central load process helps with this
? Update time – the DW is optimized for querying, not updates
? Space use: dimension tables typically take up less than 5% of DW
• So: controlled redundancy is good
? Up to a certain limit
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29
(Relational) OLAP Queries
• Two kinds of queries
? Navigation queries examine one dimension
? SELECT DISTINCT l FROM d [WHERE p]
? Aggregation queries summarize fact data
? SELECT d1.l1, d2.l2, SUM(f.m) FROM d1, d2, f
WHERE f.dk1 = d1.dk1 AND f.dk2 = d2.dk2 [AND p]
GROUP BY d1.l1,d2.l2
• Fast, interactive analysis of large amounts of data
2000 2001
Aalborg
Copenhagen
Milk
Bread
123 127
57 45
56 67
211
30
OLAP Queries
Roll-up: get overview Drilll-down: more detail
Starting level
(City, Year, Product)
Slice/Dice:
Aalborg
ALL Time
Copenhagen
Bread
Milk
Aalborg
Copenhagen
Bread
Milk
01-06
/2000
07-12
/2000
01-06
/2001
07-12
/2001
What is this value?
Aalborg
2000
Copenhagen
Bread
Milk
2000 2001
Aalborg
Copenhagen
Milk
Bread
123 127
57 45
56 67
211
16
31
OLAP Cube in MS Analysis Services Project
drill down
32
Case Study: Grocery Store
• Stock Keeping Units (SKUs)
• Point Of Sale (POS) system
• Stores
• Promotions
• Task: Analyze how promotions affect
sales
17
33
DW Design Steps
• Choose the business process(es) to model
? Sales
• Choose the granularity of the business process
? Sales by Product by Store by Promotion by Day
? Low granularity is needed
? Are individual transactions necessary/feasible?
• Choose the dimensions
? Time, Store, Promotion, Product
• Choose the measures
? Dollar_sales, unit_sales, dollar_cost, customer_count
• Resisting normalization and preserving browsing
? Flat dimension tables makes browsing easy and fast
34
The Grocery Store Dimensions
• Time dimension
? Explicit time dimension is needed (events, holidays,..)
• Product dimension
? Many-level hierarchy allows drill-down/roll-up
? Many descriptive attributes (often more than 50)
• Store dimension
? Many descriptive attributes
• Promotion dimension
? Example of a causal dimension
? Used to see if promotions work/are profitable
? Ads, price reductions, end-of-aisle displays, coupons
18
35
The Grocery Store Measures
• All additive across all dimensions
? Dollar_sales
? Unit_sales
? Dollar_cost
• Gross profit (derived)
? Computed from sales and cost: sales – cost
? Additive
• Gross margin (derived)
? Computed from gross profit and sales: (sales – cost)/cost
? Non-additive across all dimensions
• Customer_count
? Additive across time, promotion, and store
? Non-additive across product. Why?
? Semi-additive
36
Data Warehouse Size
• Estimated number of fact records:
? Time dimension: 2 years = 730 days
? Store dimension: 300 stores reporting each day
? Product dimension: 30,000 products, only 3000 sell per day
? Promotion dimension: 5000 combinations, but a product only appears
in one combination per day
? 730*300*3000*1 = 657,000,000
• Total data warehouse size: 657,000,000 facts* 8 fields/fact *
4 bytes/field = 21 GB
? Number of fields: 4 FKs + 4 measures = 8 fields
? Assuming sizes of dimensions negligible
• Small size (by today’s standard), feasible to store at transaction
level detail
19
37
Summary
• Cubes: Dimensions, Facts, Measures
• OLAP Queries
• Relational Implementation
? Star schema vs Snowflake schema
• Redundancy
•1
Advanced MD Modeling and
MD Database Implementation
2
Overview
• Handling Changes in Dimensions
• Coordinating Data Cubes / Data Marts
• Multidimensional Database Implementation
•2
4
Changing Dimensions
• In the last lecture, we assumed that
dimensions are stable over time
? New rows in dimension tables can be inserted
? Existing rows do not change
? This is not a realistic assumption
• We now study techniques for handling changes in
dimensions
• “Slowly changing dimensions” phenomenon
? Dimension information change, but changes are
not frequent
? Still assume that the schema is fixed
5
Handling Changes in Dimensions
• Handling change over time
• Changes in dimensions
? 1. No special handling
? 2. Versioning dimension values
? 2A. Special facts
? 2B. Timestamping
? 3. Capturing the previous and the current value
? 4. Split into changing and constant attributes
•3
6
Example
• Attribute values in
dimensions vary over time
? A store changes Size
? A product changes
Description
? Districts are changed
• Problems
? Dimensions not updated
?DW is not up-to-date
? Dimensions updated in a
straightforward way
?incorrect information in
historical data
TimeID
StoreID
ProductID
…
ItemsSold
Amount
ProductID
Description
Brand
PCategory
StoreID
Address
City
District
Size
SCategory
TimeID
Weekday
Week
Month
Quarter
Year
DayNo
Holiday
timeline
change
? ?
Sales fact
Time dim.
Store dim.
Product dim.
7
Example
TimeID
StoreID
ProductID
…
ItemsSold
Amount
…
StoreID
Address
City
District
Size
SCategory
…
Sales fact
Time dim.
Store dim.
Product dim.
2000
ItemsSold
001
… … StoreID
250
Size
001
… … StoreID
Sales fact table Store dimension table
The store in Aalborg has
the size of 250 sq. metres.
On a certain day,
customers bought 2000
apples from that store.
•4
8
Solution 1: No Special Handling
2000
ItemsSold
001
… … StoreID
250
Size
001
… … StoreID
2000
ItemsSold
001
… … StoreID
450
Size
001
… … StoreID
2000 001
3500
ItemsSold
001
… … StoreID
450
Size
001
… … StoreID
Sales fact table Store dimension table
The size of a store expands
A new fact arrives
What’s the problem here?
9
Solution 1
• Solution 1: Overwrite the old values in the
dimension tables
• Consequences
? Old facts point to rows in the dimension tables with
incorrect information!
? New facts point to rows with correct information
• Pros
? Easy to implement
? Useful if the updated attribute is not significant, or the old
value should be updated for error correction
• Cons
? Old facts may point to “incorrect” rows in dimensions
•5
10
Solution 2
• Solution 2: Versioning of rows with changing attributes
? The key that links dimension and fact table, identifies a version of a
row, not just a “row”
? Surrogate keys make this easier to implement
? – what if we had used, e.g., the shop’s zip code as key?
? Always use surrogate keys!!!
• Consequences
? Larger dimension tables
• Pros
? Correct information captured in DW
? No problems when formulating queries
• Cons
? Cannot capture the development over time of the subjects the
dimensions describe
? e.g., relationship between the old store and the new store not
captured
11
Solution 2: Versioning of Rows
StoreID … ItemsSold …
001 2000
StoreID … Size …
001 250
StoreID … ItemsSold …
001 2000
StoreID … Size …
001 250
002 450
StoreID … ItemsSold …
001 2000
002 3500
StoreID … Size …
001 250
002 450
different versions of a store
Which store does the
new fact (old fact) refer to?
A new fact arrives
•6
12
Solution 3
• Solution 3: Create two versions of each changing attribute
? One attribute contains the current value
? The other attribute contains the previous value
• Consequences
? Two values are attached to each dimension row
• Pros
? Possible to compare across the change in dimension value (which
is a problem with Solution 2)
? Such comparisons are interesting when we need to work
simultaneously with two alternative values
? Example: Categorization of stores and products
• Cons
? Not possible to see when the old value changed to the new
? Only possible to capture the two latest values
13
Solution 3: Two versions of Changing Attribute
StoreID … ItemsSold …
001 2000
StoreID … DistrictOld DistrictNew …
001 37 37
StoreID … ItemsSold …
001 2000
StoreID … ItemsSold …
001 2000
001 2100
StoreID … DistrictOld DistrictNew …
001 37 73
StoreID … DistrictOld DistrictNew …
001 37 73
versions of an attribute
We cannot find out when
the district changed.
•7
14
Solution 2A
• Solution 2A: Use special facts for capturing
changes in dimensions via the Time dimension
? Assume that no simultaneous, new fact refers to the
new dimension row
? Insert a new special fact that points to the new
dimension row, and through its reference to the Time
dimension, timestamps the row
• Pros
? Possible to capture the development over time of the
subjects that the dimensions describe
• Cons
? Larger fact table
15
Solution 2A: Inserting Special Facts
StoreID TimeID …ItemsSold …
001 234 2000
StoreID … Size …
001 250
StoreID … Size …
001 250
002 450
StoreID … Size …
001 250
002 450
StoreID TimeID …ItemsSold …
001 234 2000
002 345 -
StoreID TimeID …ItemsSold …
001 234 2000
002 345 -
002 456 3500
special fact for capturing changes
•8
16
Solution 2B
• Solution 2B: Versioning of rows with changing
attributes like in Solution 2 + timestamping of rows
• Pros
? Correct information captured in DW
• Cons
? Larger dimension tables
17
Solution 2B: Timestamping
StoreID TimeID …ItemsSold …
001 234 2000
StoreID Size From To
001 250 98 -
StoreID TimeID …ItemsSold …
001 234 2000
StoreID TimeID …ItemsSold …
001 234 2000
002 456 3500
StoreID Size From To
001 250 98 99
002 450 00 -
StoreID Size From To
001 250 98 99
002 450 00 -
attributes: “From”, “To”
•9
18
Example of Using Solution 2B
• Product descriptions are versioned, when
products are changed, e.g., new package sizes
? Old versions are still in the stores, new facts can refer
to both the newest and older versions of products
? Time value for a fact not necessarily between “From”
and “To” values in the fact’s Product dimension row
• Unlike changes in Size for a store, where all facts
from a certain point in time will refer to the newest
Size value
• Unlike alternative categorizations that one wants
to choose between
19
Rapidly Changing Dimensions
• Difference between “slowly” and “rapidly” is subjective
? Solution 2 is often still feasible
? The problem is the size of the dimension
• Example
? Assume an Employee dimension with 100,000 employees, each
using 2K bytes and many changes every year
? Solution 2B is recommended
• Examples of (large) dimensions with many changes:
Product and Customer
• The more attributes in a dimension table, the more
changes per row are expected
• Example
? A Customer dimension with 100M customers and many attributes
? Solution 2 yields a dimension that is too large
•10
20
Solution 4: Dimension Splitting
CustID
Name
PostalAddress
Gender
DateofBirth
Customerside
…
NoKids
MaritialStatus
CreditScore
BuyingStatus
Income
Education
…
DemographyID
NoKids
MaritialStatus
CreditScoreGroup
BuyingStatusGroup
IncomeGroup
EducationGroup
…
CustID
Name
PostalAddress
Gender
DateofBirth
Customerside
…
Customer dimension (original)
Customer
dimension (new):
relatively static
attributes
Demographics
dimension:
often-changing
attributes
21
Solution 4
• Solution 4
? Make a “minidimension” with the often-changing (demograhic)
attributes
? Convert (numeric) attributes with many possible values into
attributes with few discrete or banded values
? E.g., Income group: [0,10K), [0,20K), [0,30K), [0,40K)
? Why? Any Information Loss?
? Insert rows for all combinations of values from these new domains
? With 6 attributes with 10 possible values each, the dimension gets
10
6
=1,000,000 rows
? If the minidimension is too large, it can be further split into more
minidimensions
? Here, synchronous/correlated attributes must be considered (and
placed in the same minidimension)
? The same attribute can be repeated in another minidimension
•11
22
Solution 4 (Changing Dimensions)
• Pros
? DW size (dimension tables) is kept down
? Changes in a customer’s demographic values do not
result in changes in dimensions
• Cons
? More dimensions and more keys in the star schema
? Navigation of customer attributes is more cumbersome
as these are in more than one dimension
? Using value groups gives less detail
? The construction of groups is irreversible
23
Changing Dimensions - Summary
• Why are there changes in dimensions?
? Applications change
? The modeled reality changes
• Multidimensional models realized as star schemas
support change over time to a large extent
• A number of techniques for handling change over
time at the instance level was described
? Solution 2 and the derived 2A and 2B are the most
useful
? Possible to capture change precisely
•12
24
• Coordinating Data Cubes / Data Marts
25
DW Bus Architecture
• What method for DW construction?
? Everything at once, top-down DW (”monoliths”)
? Separate, independent marts (”stovepipes”, ”data islands”)
• None of these methods work in practice
? Both have different ”built-in” problems
• Architecture-guided step-by-step method
? Combines the advantages of the first two methods
• A data mart can be built much faster than a DW
? ETL is always the hardest - minimize risk with a simple mart
? But: data marts must be compatible
? Otherwise, incomparable views of the enterprise result
• Start with single-source data marts
? Facts from only one source makes everything easier
•13
26
DW Bus Architecture
• Data marts built independently by departments
? Good (small projects, focus, independence,…)
? Problems with ”stovepipes” (reuse across marts impossible)
• Conformed dimensions and facts/measures
• Conformed dimensions
? Same structure and content across data marts
? Take data from the best source
? Dimensions are copied to data marts (not a space problem)
• Conformed fact definitions
? The same definition across data marts (price excl. sales tax)
? Observe units of measurement (also currency, etc.)
? Use the same name only if it is exactly the same concept
? Facts are not copied between data marts (facts > 95% of data)
• This allows several data marts to work together
? Combining data from several fact tables is no problem
27
DW Bus Architecture
• Dimension content managed by dimension owner
? The Customer dimension is made and published in one place
• Tools query each data mart separately
? Separate queries to each data mart
? Results combined by tool or OLAP server
• It is hard to make conformed dimensions and facts
? Organizational and political challenge, not technical
? Get everyone together and
? Get a top manager (CIO) to back the conformance decision.
? No-one must be allowed to ”escape”
• Exception: if business areas are totally separate
? No common management/control
•14
28
Large Scale Cube Design
• The design is never ”finished”
? The dimensional modeler is always looking for new information to
include in dimensions and facts
? A sign of success!
• New dimensions and measures introduced gracefully
? Existing queries will give same result
? Example: Location dimension can be added for old+new facts
? Can usually be done if data has sufficiently fine granularity
• Data mart granularity
? Always as fine as possible (transaction level detail)
? Makes the mart insensitive to changes
29
Coordinating Data Marts
• Multi-source data marts
? Not built initially due to too large complexity
? Combine several single-source data marts (building blocks)
? Built ”on top of” several single-source marts
? Relatively simple due to conformed dimensions and facts
? Can be done physically or virtually (in OLAP server)
? Example: profitability data mart
? Important to have fine (single transaction?) granularity
•15
30
Matrix Method
• DW Bus Architecture Matrix
• Two-dimensional matrix
? X-axis: dimensions
? Y-axis: data marts
• Planning Process
? Make list of data marts
? Make list of dimensions
? Mark co-occurrences (which marts have which dimensions)
? Time dimension occurs in (almost) all marts
31
Matrix Example
Time Customer Product Supplier
Sales + + +
Costs + + +
Profit + + + +
•16
32
• Multidimensional database implementation
? MS SQL Server
? MS Analysis Services
33
MS SQL Server 2008
• Microsoft’s RDBMS
? Runs on Windows OS only
• Nice features built-in
? Analysis Services
? Integration Services
? Reporting Services
• Easy to use
? Graphical “Management Studio” and “BI Developer
Studio”
? Watch the demonstration videos from Microsoft to get a
quick introduction
•17
34
MS Analysis Services
• Cheap, easy to use, good, and widely used
• Support ROLAP, MOLAP, HOLAP technology
• Intelligent pre-aggregation (for improving query
performance)
• Programming: MS OLE DB for OLAP interface
• Uses the query language MDX (MultiDimensional
eXpressions)
35
Summary
• Handling Changes in Dimensions
• Coordinating Data Cubes / Data Marts
• Multidimensional Database Implementation:
MS SQL Server and Analysis Services
•1
Extract, Transform, Load (ETL)
ETL Overview
• The ETL Process
• General ETL issues
? Building dimensions
? Building fact tables
? Extract
? Transformations/cleansing
? Load
• SQL Server Integration Services
2
•2
The ETL Process
• The most underestimated process in DW development
• The most time-consuming process in DW development
? Up to 80% of the development time is spent on ETL!
• Extract
? Extract relevant data
• Transform
? Transform data to DW format
? Build DW keys, etc.
? Cleansing of data
• Load
? Load data into DW
? Build aggregates, etc.
3
Phases
• Design phase
? Modeling, DB design, source selection,…
• Loading phase
? First load/population of the DW
? Based on all data in sources
• Refreshment phase
? Keep the DW up-to-date wrt. source data changes
4
•3
ETL/DW Refreshment
Integration
phase
Preparation
phase
DW
DM
5
ETL in the Architecture
Data Staging
Area
ETL side Query side
Query Services
- Extract
- Transform
- Load
Data mining
Data
sources
Operational
system
Desktop Data
Access Tools
Reporting Tools
Data marts with
aggregate-only data
Data
Warehouse
Bus
Conformed
dimensions
and facts
Data marts with
atomic data
-Warehouse Browsing
-Access and Security
-Query Management
- Standard Reporting
-Activity Monitor
6
•4
Data Staging Area (DSA)
• Transit storage for data in the ETL process
? Transformations/cleansing done here
• No user queries
• Sequential operations on large data volumes
? Performed by central ETL logic
? Easily restarted
? No need for locking, logging, etc.
? RDBMS or flat files? (DBMS have become better at this)
• Finished dimensions copied from DSA to relevant marts
• Allows centralized backup/recovery
? Backup/recovery facilities needed
? Better to do this centrally in DSA than in all data marts
7
ETL Construction Process
? Plan
1) Make high-level diagram of source-destination flow
2) Test, choose and implement ETL tool
3) Outline complex transformations, DW key generation and
job sequence for every destination table
? Construction of dimensions
4) Construct and test building static dimension
5) Construct and test change mechanisms for one dimension
6) Construct and test remaining dimension builds
? Construction of fact tables and automation
7) Construct and test initial fact table build
8) Construct and test incremental update
9) Construct and test aggregate build
10) Design, construct, and test ETL automation
8
•5
High-level diagram
1) Make high-level diagram of source-destination flow
? Mainly used for communication purpose
? One page only, highlight sources and destinations
? Steps: extract, transform, load
Raw-Product
(Spreadsheet)
Raw-Sales
(RDBMS)
Product
Sales
Add product
type
Aggregate sales
per product per day
Check R.I.
Extract time
Time
Source
Destination
9
Building Dimensions
• Static dimension table
? DW key assignment: production keys to DW keys
using table
? Check one-one and one-many relationships (using
sorting)
• Handling dimension changes
? Described in last lecture
? Find the newest DW key for a given production key
? Table for mapping production keys to DW keys
must be maintained and updated
• Load of dimensions
? Small dimensions: replace
? Large dimensions: load only changes
Product dimension of FClub vs.
Product dimension of a supermarket
pid DW_pid Time
11 1 100
22 2 100
35 3 200
11 4 700
…… …… ……
Key mapping for the
Product dimension
10
•6
Building Fact Tables
• Two types of load
• Initial load
? ETL for all data up till now
? Done when DW is started the first time
? Very heavy - large data volumes
• Incremental update
? Move only changes since last load
? Done periodically (e.g., month or week) after DW start
? Less heavy - smaller data volumes
• Dimensions must be updated before facts
? The relevant dimension rows for new facts must be in place
? Special key considerations if initial load must be performed again
Years 01-08
Jan
09
Feb
09
March
09
11
Extract
12
•7
Types of Data Sources
• Non-cooperative sources
? Snapshot sources – provides only full copy of source, e.g., files
? Specific sources – each is different, e.g., legacy systems
? Logged sources – writes change log, e.g., DB log
? Queryable sources – provides query interface, e.g., RDBMS
• Cooperative sources
? Replicated sources – publish/subscribe mechanism
? Call back sources – calls external code (ETL) when changes occur
? Internal action sources – only internal actions when changes occur
? DB triggers is an example
• Extract strategy depends on the source types
13
Extract
• Goal: fast extract of relevant data
? Extract from source systems can take long time
• Types of extracts:
? Extract applications (SQL): co-existence with other applications
? DB unload tools: faster than SQL-based extracts
? e.g., MS SQL Export Wizard, MySQL DB dump
• Extract applications the only solution in some scenarios
• Too time consuming to ETL all data at each load
? Can take days/weeks
? Drain on the operational systems and DW systems
• Extract/ETL only changes since last load (delta)
14
•8
Computing Deltas
• Delta = changes since last load
• Store sorted total extracts in DSA
? Delta can easily be computed from current + last
extract
? + Always possible
? + Handles deletions
? - High extraction time
• Put update timestamp on all rows (in sources)
? Updated by DB trigger
? - Source system must be changed, operational overhead
? Extract only where “timestamp > time for last extract”
? + Reduces extract time
? - Cannot (alone) handle deletions.
Timestamp DKK …
100 10 …
200 20 …
300 15 …
400 60 …
500 33 …
Last extract
time: 300
15
Changed Data Capture
• Messages
? Applications insert messages in a ”queue” at updates
? + Works for all types of updates and systems
? - Operational applications must be changed+operational overhead
• DB triggers
? Triggers execute actions on INSERT/UPDATE/DELETE
? + Operational applications need not be changed
? + Enables real-time update of DW
? - Operational overhead
• Replication based on DB log
? Find changes directly in DB log which is written anyway
? + Operational applications need not be changed
? + No operational overhead
? - Not possible in some DBMS (SQL Server, Oracle, DB2 can do it)
16
•9
Transform
17
Common Transformations
• Data type conversions
? EBCDIC ?ASCII/Unicode
? String manipulations
? Date/time format conversions
? E.g., Unix time 1201928400 = what time?
• Normalization/denormalization
? To the desired DW format
? Depending on source format
• Building keys
? Table matches production keys to surrogate DW keys
? Correct handling of history - especially for total reload
18
•10
Data Quality
• Data almost never has decent quality
• Data in DW must be:
? Precise
? DW data must match known numbers
? Complete
? DW has all relevant data
? Consistent
? No contradictory data: aggregates fit with detail data
? Unique
? The same thing is called the same and has the same key
? Timely
? Data is updated ”frequently enough” and the users know when
19
Cleansing
• Why cleansing? Garbage In Garbage Out
• BI does not work on “raw” data
? Pre-processing necessary for BI analysis
• Handle inconsistent data formats
? Spellings, codings, …
• Remove unnecessary attributes
? Production keys, comments,…
• Replace codes with text for easy understanding
? City name instead of ZIP code, e.g., Aalborg vs. DK-9000
• Combine data from multiple sources with common key
? E.g., customer data from customer address, customer name, …
20
•11
Types of Cleansing
• Conversion and normalization
? Most common type of cleansing
? Text coding, date formats
? does 3/2 mean 3
rd
February or 2
nd
March?
• Special-purpose cleansing
? Look-up tables, dictionaries to find valid data, synonyms, abbreviations
? Normalize spellings of names, addresses, etc.
? Dorset Rd or Dorset Road? København or Copenhagen? Aalborg or Ålborg?
? Remove duplicates, e.g., duplicate customers
• Domain-independent cleansing
? Approximate, “fuzzy” joins on records from different sources
? E.g., two customers are regarded as the same if their respective values
match for most of the attributes (e.g., address, phone number)
• Rule-based cleansing
? User-specified rules: if-then style
? Automatic rules: use data mining to find patterns in data
? Guess missing sales person based on customer and item
21
Cleansing
• Don’t use “special” values (e.g., 0, -1) in your data
? They are hard to understand in query/analysis operations
• Mark facts with Data Status dimension
? Normal, abnormal, outside bounds, impossible,…
? Facts can be taken in/out of analyses
• Uniform treatment of NULL
? Use NULLs only for measure values (estimates instead?)
? Use special dimension key (i.e., surrogate key value) for
NULL dimension values
? E.g., for the time dimension, instead of NULL, use special key
values to represent “Date not known”, “Soon to happen”
? Avoids problems in joins, since NULL is not equal to NULL
Sales SID …
10 1 …
20 1 …
10000 2 …
-1 3 …
Sales fact table
SID Status
1 Normal
2 Abnormal
3 Out of bounds
… …
Data Status
Dimension
22
•12
Improving Data Quality
• Appoint “data steward”
? Responsibility for data quality
? Includes manual inspections and corrections!
• DW-controlled improvement
? Default values
? ”Not yet assigned 157” note to data steward
• Source-controlled improvements
• Construct programs that check data quality
? Are totals as expected?
? Do results agree with alternative source?
? Number of NULL values?
23
Load
24
•13
Load
• Goal: fast loading into DW
? Loading deltas is much faster than total load
• SQL-based update is slow
? Large overhead (optimization, locking, etc.) for every SQL call
? DB load tools are much faster
• Index on tables slows load a lot
? Drop index and rebuild after load
? Can be done per index partition
• Parallellization
? Dimensions can be loaded concurrently
? Fact tables can be loaded concurrently
? Partitions can be loaded concurrently
25
Load
• Relationships in the data
? Referential integrity and data consistency must be ensured
before loading
? Because they won’t be checked in the DW again
? Can be done by loader
• Aggregates
? Can be built and loaded at the same time as the detail data
• Load tuning
? Load without log
? Sort load file first
? Make only simple transformations in loader
? Use loader facilities for building aggregates
26
•14
ETL Tools
• ETL tools from the big vendors
? Oracle Warehouse Builder
? IBM DB2 Warehouse Manager
? Microsoft SQL Server Integration Services (SSIS)
• Offers much functionality
? Data modeling
? ETL code generation
? Scheduling DW jobs
• … but (some) have steep learning curves and high
costs
• The “best” tool does not exist
? Choose based on your own needs
? You may also have to code your own
27
Issues
• Pipes
? Redirect output from one process to input of another process
cat payments.dat | grep 'payment' | sort -r
• Files versus streams/pipes
? Streams/pipes: no disk overhead, fast throughput
? Files: easier restart, often only possibility
• Use ETL tool or write ETL code
? Code: easy start, co-existence with IT infrastructure, maybe the
only possibility
? Tool: better productivity on subsequent projects,
“self-documenting”
• Load frequency
? ETL time dependent of data volumes
? Daily load is much faster than monthly
? Applies to all steps in the ETL process
28
•15
SQL Server Integration Services
• A concrete ETL tool
• Example ETL flow
29
Integration Services (IS)
• Microsoft’s ETL tool
? Part of SQL Server 2008
• Tools
? Import/export wizard - simple transformations
? BI Development Studio - advanced development
• Functionality available in several ways
? Through GUI - basic functionality
? Programming - advanced functionality
30
•16
Packages
• A package is a collection of
? Data flows (Sources ?Trans-
formations ?Destinations)
? Connections
? Control flow: Tasks, Workflows
? Variables
? …
• A package may also invoke
other packages and/or
processes
• It is somehow similar to a
“program”
31
A package
32
Arrows show precendence
constraints
Constraint values:
• success (green)
• failure (red)
• completion (blue)
Conditional expressions may
also be given (A > B)
•17
Package Control Flow
• Containers provide
? Structure to packages
? Services to tasks
• Control flow
? Foreach loop container
? Repeat tasks by using an enumerator
? For loop container
? Repeat tasks by testing a condition
? Sequence container
? Groups tasks and containers into
control flows that are subsets of the
package control flow
• Task host container
? An abstract container class which is
used implicitly
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Tasks
• A task is a unit of work
• Workflow Tasks
? Execute package – execute other SSIS packages, good for structure!
? Execute Process – run external application/batch file
• SQL Servers Tasks
? Bulk insert – fast load of data
? Execute SQL – execute any SQL query
• Data Preparation Tasks
? File System – operations on files
? FTP – up/download data
• Scripting Tasks
? Script – execute .NET code
• Maintenance Tasks – DB maintenance
• Data Flow Tasks – run data flows from sources through
transformations to destinations (this is where the work is done)
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Data Flow Elements
• Sources
? Make external data available
? All ODBC/OLE DB data sources:
RDBMS, Excel, Text files, ……
• Transformations
? Update, summarize, cleanse,
merge
• Destinations
? Write data to specific store
• Input, Output, Error output
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Transformations
• Row Transformations
? Character Map - applies string functions to character data
? Derived Column – populates columns using expressions
• Rowset Transformations (rowset = tabular data)
? Aggregate - performs aggregations
? Sort - sorts data
? Percentage Sampling - creates sample data set by setting %
• Split and Join Transformations
? Conditional Split - routes data rows to different outputs
? Merge - merges two sorted data sets
? Lookup Transformation - looks up ref values by exact match
• Other Transformations
? Export Column - inserts data from a data flow into a file
? Import Column - reads data from a file and adds it to a data flow
? Slowly Changing Dimension - configures update of a SCD
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A Few Hints on ETL Design
• Don’t implement all transformations in one step!
? Build first step and check that result is as expected
? Add second step and execute both, check result (How to check?)
? Add third step ……
• Test SQL statements before putting into IS
• Do one thing at the time
? Copy source data one-by-one to the data staging area (DSA)
? Compute deltas
? Only if doing incremental load
? Handle versions and DW keys
? Versions only if handling slowly changing dimensions
? Implement complex transformations
? Load dimensions
? Load facts
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Summary
• The ETL Process
• Extract
• Transformations/cleansing
• Load
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doc_914939538.pdf