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Building Data Warehouse

Building Data Warehouse . Zhenhao Qi Department of Biochemistry & Department of Computer Science and Engineering State University of New York at Buffalo March 23rd, 2000. Outline: 1. Migrating data from legacy systems: an iterative, incremental methodology.

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Building Data Warehouse

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  1. Building Data Warehouse Zhenhao Qi Department of Biochemistry & Department of Computer Science and Engineering State University of New York at Buffalo March 23rd, 2000

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. Four Dimensions of Metadata: Another way of looking at the sources of technical challenge with respect to the type of metadata required to minimize the impact of change. The need to adapt to change, error and complexity is regular over time can be seen like waves on a beach. change time complexity error

  7. 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.

  8. 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

  9. 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.

  10. On the lack of an integrated development environment Query tools Data discover and cleansing tools Operational systems Datamart Servers Raw data IMS Corrected data Data Data conversion Tool Data Warehouse DBMS Servers DB2 Replication Tools Metadata IDMS Case Tools Repositories

  11. 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 regarding • The number of systems and tools that must be interfaced. • The types of metadata required for the meta-model. • The best versioning strategy for performing impact analysis. • The desired set of functionality for automating this process.

  12. 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.

  13. 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

  14. What is high quality data ? • Addressability. • Domain integrity. • Be accurate. • Be properly integrated to attain entity integrity. • Adhere to business rules. • Satisfy business needs. • Integrity. • Be consistent. • Data redundancy must be intentional. • Be complete. • Correct cardinality.

  15. Data reengineering: a four-phase process to attain high data quality External Files • Customer • information • System • Data • Warehouses • Client/Server • Application • EISs 1. Data investigation 2. Data conditioning & Standardization 3. Data Integration 4. Data survivorship and Formatting Legacy Application Historical Extracts

  16. Data investigation • Parsing • Lexical analysis • Pattern investigation • Data typing • Data integration • The integration phase identifies and consolidates related records • lacking common keys through statistical matching technique • Flexible construction of search keys to optimize machine resources • Flexible definition of match fields to increase data points for statistical analysis • Variable weights and penalties for each data point to take into account an organization’s business rules and produces scores relative to probability.

  17. 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

  18. Symmetric multiprocessor (SMP) • 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. CPUs One hop One hop Shared symmetric system bus Shared memory Disks

  19. Loosely Coupled Architectures • The 2-D mesh It has a connection density of 4, in that each node is attached to at most four of its neighbors. CPU Mem Disk CPU Mem Disk CPU Mem Disk CPU Mem Disk CPU Mem Disk CPU Mem Disk Auxiliary Disk Auxiliary Disk Auxiliary Disk

  20. 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. All nodes are only one hop away from one another Switch element Switch element Node Connections Are typically Bi-directional And Non-blocking Switch element Switch element A direct connection between each node and every other node

  21. 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 *)

  22. The Machine architecture is causing the data skew FTS FTS Sort Disk Disk 5 3 1 Sort FTS Sort Disk 6 4 2 • 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)

  23. SMPs: No machine-architecture-induced data skew FTS FTS FTS Sort Sort Sort CPUs Shared system bus FTS(1) FTS(2) FTS(3) Sort(4) Sort(5) Sort(6) Disk Disk Shared memory

  24. Crossbar switch connection models Eliminate data skew All nodes are directly connected to all other nodes via crossbar switch. There is only one hop to and from any destination, guaranteeing uniform data access times. Mem Mem CPU CPU Disk Disk Crossbar Switch Mem Mem CPU CPU Disk Disk Mem Mem CPU CPU Disk Disk

  25. Refinements to the crossbar switch architecture 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. Disk SMP SMP Disk Crossbar Switch Disk SMP SMP Disk Disk SMP SMP Disk

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