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7/29/2019 Accessing the Data warehouse.ppt
http://slidepdf.com/reader/full/accessing-the-data-warehouseppt 1/14
Accessing the Data
Warehouse
7/29/2019 Accessing the Data warehouse.ppt
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Introduction
Purpose of building a DW
To provide user access to a set of data
Requires Tools
Aim
Aid the readers to understand accessing DW and
the major issues associated with it
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Choosing an access tool
Golden rules
Never design the DW to suit a specific tool
Make no assumptions about the type of tool that
will be used
DW must be designed for
Efficiency of query access
Ease of management
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Types of Tool
Data dipping
Basic business tools
Generates standard business reports
Data mining
Specialist tools designed for finding trends and
patterns
Data analysis
Performs complex analysis of data
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Data dipping tools
Perform basic analysis
Answers standard business questions
What were the sales last month?
How many new customers joined last month?
Have reasonable drill-down capabilities
Use meta data to present a business friendly
schema to the user
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Data mining tools
Use techniques like AI and NN
Identifies common behavioral trends
Customer buying pattern, customer groups to root
out market segmentss
Users of this tool
Analyst who have good knowledge of the business
data
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Data analysis tools
Performs sophisticated analysis
Understand common business metrics
Market share, profitability
Types
ROLAPTraditional SQL-oriented tools that have tightintegration to the relational model
MOLAP
Tools that use matrix arithmetic and sparse matrixoptimization
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Multidimensional analysis
Data can be analyzed in many dimensions
Advantage of MD tools
Fast on predefined set of data
Operations like time series analysis, top-ten and
bottom-ten selection are faster
Dimensional slicing is good
Disadvantages
Loading the cube is very slow
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Key periods
Important reporting and financial periods
Does your business require short term analysis or long term
Key business dates Need to be documented
Set of holidays, Start of company’s business year, Start of the local taxyear
Business times
Business week day working hours, Weekend working practices,Starting day of business week
Capture all the time, period and business date information asthey crucially affect the DW design
Period information must be captured by department, group and
many other organizational divisions
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Meaningful level of data
How much detailed information is required on each key
dimension?
What level of details does data need to go?
Document the answers
Get the level of detail at which data must be stored
correctIf this decision is made incorrectly the DW will need
to be completely reorganized
Having understood all these details willRound out the query requirements
Query sort estimates can be calculated
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Query categories
Batch reports
Well understood requirementsRun offline and scheduled around any daily access andovernight process
Canned queriesPredefined queries
Run online
Data set size they access vary
Ad hoc queriesUnpredictable elements
DW must run any query when desired and expect areasonable response
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Designing to the query requirements
To make a DW to be query efficient even for
ad hoc access
Star schemas
Denormalization
Query parallelism
Data partitioning
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Avoiding inefficient queries
To avoid query occupying the entire resource and
that runs for ever Use profiles
– Limits the amount of resource a user process can use
– If the limit exits stop processing the query
– DISADV: can be build only after wasting lot of resource
Queuing of queries
– All queries must be submitted via query manger
» Degree of parallelism increases
» Other resources of the query can be controlled