building data warehouse zhenhao qi department of biochemistry & department of computer science...
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Building Data Warehouse
Zhenhao QiDepartment of Biochemistry & Department of Computer Science and EngineeringState University of New York at Buffalo
March 23rd, 2000
Outline:
1. Migrating data from legacy systems: an iterative, incremental methodology.
2. Building high data quality into data warehouse.
3. Optimal machine architectures for parallel query scalability
The difficulties of migrating data from legacy systems:
1. The same data is presented differently in different system.
2. The schema for a single database may not be consistent over time.
3. Data may simply be bad.
4. The data values are not represented in a form that is meaningful to
end users.
5. Conversions and migrations in heterogeneous environments typically
involve data from multiple incompatible DBMS and hardware platform.
6. The execution windows for data conversion programs must be
coordinated carefully in order to provide the new applications with a
consistent view of data without impacting production system.
The need for an iterative, incremental methodology
IT organizations failed to use the same serial methodology for large migration problems that they used for relatively discrete projects.
1. In a large organization, the complexity of the analysis and design can involve person-years of effort without any demonstrable results.
2. The sheer complexity of both the data analysis and the design of the target system has prevented effective progress.
3. The rate of change in operational systems has outstripped the migration team’s ability to keep current.
Implications for metadata and the data warehouse
3 sources of change that the data warehouse team must anticipate
• Those arising from the normal regular changes to operational system.
• Those that result from using an iterative, incremental methodology.
• Those that result from external business drivers like acquisition.
The key to dealing with change cost effectively lies in metadata.
Four Dimensions of Metadata:
Another way of looking at the sources of technical challengewith respect to the type of metadata required to minimizethe impact of change. The need to adapt to change, error andcomplexity is regular over time can be seen like waves ona beach.
change time
complexityerror
Metadata that capture the current environment
• The record and data element definitions.• Inter- and intra- database relationships.• A definition of each interface program used to build or refresh the warehouses a. which inter-database joins it uses; b. the timing and direction of the execution; c. any execution parameters d. dependencies on any other interface programs e. the use or production of other ancillary database f. the name and location of the file that contains the source code for this data interface program. g. the tool and session name if this interface program was automatically generated.
Metadata required to reduce the cost of errors
The meta-model should allow the inclusion of the information discovered at the level of data element, record, database, and join. This information includes but is not limited to:
•legal ranges and values•any exception logic that the data interface program should take if an illegal value is found
Metadata that can reduce the cost of complexity Other types of metadata may be needed to reduce the complexity of specifying, maintaining, and executing the data interface program Factoring in time 1. Information about each database that can effect execution time such as: a. Database size and volatility. b. The time window during which each database can be accessed. c. The mechanism that should be used for changed data capture. 2. Versioning a. Design the meta-model to anticipate change. b. Choose tools that provide sufficient versioning support to facilitate input analysis.
On the lack of an integrated development environment
IMS
DB2
IDMS
Operational systems
Data conversion Tool
Data discoverand cleansing tools
Query tools
Rawdata
Corrected data
Case Tools Repositories
Warehouse DBMS Servers
Replication Tools
Metadata
Data
Data
Datamart Servers
Developing an evaluation grid
The best strategy would be to • create a list of the types of change the organization is most likely to encounter.• determine the types of metadata required to respond to this change cost effectively.From this data one should be able to determine a set of requirements regarding1. The number of systems and tools that must be interfaced.2. The types of metadata required for the meta-model.3. The best versioning strategy for performing impact analysis.4. The desired set of functionality for automating this process.
The greatest cost benefit of high data quality
• Data quality assures previously unavailable competitive advantage and strategic capability
1. Improved accuracy, timeliness, and confidence in decision making. 2. Improved customer service and retention. 3. Unprecedented sales and marketing opportunities. 4. Support for business reengineering initiatives
• High data quality improves productivity
1. Enables smart corporate-wide purchasing strategies. 2. Streamlines work process.
• High data quality reduces costs
1. Reduces physical inventory by identifying anomalies and redundancies within manufacturing parts, pharmaceutical prescription drugs, and so on. 2. Simplifies database management and reduces storage requirements for information system. 3. Reduces mailing and production costs. 4. Reduces clerical staff. 5. Spares costly redesigns of data models.
• The absence of high data quality precludes effective use of new systems
• High data quality avoids the compounding effect of data contamination
What is high quality data ?
1. Addressability.2. Domain integrity.3. Be accurate.4. Be properly integrated to attain entity integrity.5. Adhere to business rules.6. Satisfy business needs.7. Integrity.8. Be consistent.9. Data redundancy must be intentional.10. Be complete.11. Correct cardinality.
Data reengineering: a four-phase process to attain high data quality
External Files
LegacyApplication
HistoricalExtracts
1. Data investigation2. Data conditioning & Standardization3. Data Integration4. Data survivorship and Formatting
•Customer information System
•Data Warehouses
•Client/Server Application
•EISs
• Data investigation
ParsingLexical analysisPattern investigationData typing
• Data integration
The integration phase identifies and consolidates related records lacking common keys through statistical matching techniqueFlexible construction of search keys to optimize machine resourcesFlexible definition of match fields to increase data points for statistical analysisVariable weights and penalties for each data point to take into account an organization’s business rules and produces scores
relative to probability.
Why uniform data access times are optimal for parallel query execution?
• Algorithmic parallelism is achieved using a paradigm similar to the division of labor• The material (data) must be evenly distributed among the personnel (CPUs) or the effect of parallelism is lost
Symmetric multiprocessor (SMP)
CPUs
Shared symmetric system bus
Sharedmemory
Disks
One hopOne hop
•A classical SMP is a tightly coupled connection model where all components connected to a single bus are equidistant.•Disadvantage: very short buses limit scalability.
Loosely Coupled Architectures
• The 2-D mesh
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
Auxiliary Disk
Auxiliary Disk
Auxiliary Disk
It has a connection density of 4, in that each node is attached toat most four of its neighbors.
• Crossbar Switch Borrowed from telephony, this technology creates a direct, point-to-point connection between every node, with only one hop through the switch to get from one node to any other node.
Switchelement
Switchelement
Switchelement
Switchelement
NodeConnectionsAre typicallyBi-directionalAndNon-blocking
A direct connection between each node and every other node
All nodes are only one hop away from one another
A query parse tree example
SELECT * FROM Table_a ORDER BY Column_2
Concatenation merge of sorted runs into result
Parallel sort on column_2 (ORDER BY)
Parallel full table scan of Table_a (SELECT *)
The Machine architecture is causing the data skew
Disk
SortFTS
SortSort
Disk
FTS
Disk
FTS
1
2
3
4
5
6
•Sort(5) receives data from FTS(1) in two hops (there are 50As)•Sort(5) receives data from FTS(2) in three hops (there are 100As)•Sort(5) receives data from FTS(3) in one hops (there are 10As)
SMPs: No machine-architecture-induced data skew
FTS FTS FTS Sort Sort Sort
FTS(1) FTS(2) FTS(3) Sort(4) Sort(5) Sort(6)Disk Disk
CPUs
Shared system bus
Shared memory
Crossbar switch connection models Eliminate data skew
CPU Mem
Disk
CPUMem
Disk
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
CPU Mem
Disk
CrossbarSwitch
All nodes are directly connected to all other nodes via crossbar switch.There is only one hop to and from any destination, guaranteeing uniformdata access times.
Refinements to the crossbar switch architecture
CrossbarSwitch
SMP SMP
SMP SMP
SMP SMP
Disk
Disk
Disk
Disk
Disk
Disk
1. Share the disk drives using the switch, thus combining the virtues of shared nothing and clustered architectures in one architecture.2. Make the loosely coupled nodes symmetric multiprocessors.
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