1. M ODULE 5 Metadata, Tools, and Data Warehousing Section 4 Data Warehouse Administration 1 ITEC 450

2. D ATA W AREHOUSE AND C HARACTERISTICS A data warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data that is designed for query and analysis rather than for transaction processes. Subject-oriented – data pertains to a particular subject instead of the many subjects pertinent to the company’s ongoing operations. Integrated – consistent naming conventions, formats, encoding structures; from multiple data sources Time-variant – data is identified with a particular time period, can study trends and changes Non-updatable – data is stable in a data warehouse. Data loaded, and should not be removed. 2 ITEC 450

3. C OMPARISON OF D ATABASE C HARACTERISTICS 3 ITEC 450

4. D ATA W AREHOUSE AND B USINESS I NTELLIGENCE A data warehouse usually contains historical data derived from transaction data and other sources. It enables an organization to consolidate data. It includes An extraction, transportation, transformation, and loading (ETL) solution An online analytical processing (OLAP) engine Client analysis tools Reporting 4 ITEC 450

5. A NALYTICAL VS. T RANSACTION P ROCESSING Analytical processing – informational systems DSS – decision support system OLAP – online analytical processing Data mining – the process of mining or discovery of new information in terms of patterns or rules from vast amounts of data Transaction processing – operational system OLTP – online transaction processing 5 ITEC 450

6. D ATA W AREHOUSE D ESIGN Star schema - data modeling technique used to map multidimensional decision support data into a relational database. It is ex cellent for ad-hoc queries, but bad for online transaction processing. It contains four components: Fact table Dimension tables Attributes Attribute hierarchies Snowflake schema – a star schema in which the dimension tables have additional relationships 6 ITEC 450

7. S TAR S CHEMA C OMPONENTS 7 ITEC 450

8. S TAR S CHEMA E XAMPLE 8 ITEC 450

9. D ATA M OVEMENT – ETL P ROCESS ETL – Extract, Transform, and Load Capture – extract or obtaining a snapshot of a chosen subset of the source data for loading into the data warehouse Scrub or data cleansing – us es pattern recognition and AI techniques to upgrade data quality Transform – c onvert data from format of operational system to format of data warehouse Load – p lace transformed data into the warehouse and create indexes 9 ITEC 450

10. D ATA W AREHOUSE P ERFORMANCE Perspectives of data warehouse performance Extract performance – how ETL process performs Data management – database design and data quality Query performance – OLAP tuning Server performance – hardware support Automated summary tables Provide a proper set of aggregate information Commonly implement with materialized views or batch operation tables DBMS features to support data warehousing Materialized views – automatically creation of summaries Bitmap indexes – widely used in data warehousing, in addition to B-tree Parallel execution – multiple processes work together simultaneously to run a single SQL statement 10 ITEC 450

11. M ODULE 5 Metadata, Tools, and Data Warehousing Section 5 DBA Rules of Thumb 11 ITEC 450

12. T HE R ULES OF T HUMB Personal DBA handbook Write down your own experience Categorize them in a searchable note or repository Backup everything and plan for worst all the time Before making any changes, ensure that you can recover from them Automation and share your knowledge Create a systematic way to troubleshoot problems Create, reuse and share scripts Knowledge sharing will open many revenues for you Next levels Understand the business, not just the technology Keep up-to-date on technology 12 ITEC 450

13. C OURSE S UMMARY (Y OUR L EARNING ) DBA Roles and Responsibilities DBMS Architecture, Physical and Logical Structures DBMS Installation and Database Creation Database Connectivity and Network Components Database Security and Audit Capability Database Backup and Recovery Database Monitoring, DBMS System Tuning, Physical Configuration Optimization SQL Query Coding and Tuning, Data Loading Database Metadata, Data Dictionary Data Warehouse Characteristics and Overview 13 ITEC 450