Data warehouse foundation training v1.1
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Data warehouse foundation training v1.1
- 1. Hitachi Consulting Franky Lao 5/17/2013 Data Warehouse Foundation Training BetterBetter
- 2. Agenda • Why need BI/DW • What is BI/DW • How to implement BI/DW • Fashion Terms (optional)
- 4. What is BI/DW (cont.) • Concept of Business Intelligence • Concept of Data Warehouse • Differences of DW and DB
- 6. How to implement DW • Modeling • ETL • Reporting
- 7. Modeling 2 patterns of modeling: • - Normalized Model • - Dimensional Model (More preferred)
- 8. E.T.L. (Process) • Extract • Transform • Load
- 9. Reporting • Ad-hoc reporting • Cube • Data Mining (huge topic)
- 10. Fashion Terms (optional) • OLTP / OLAP • ELT • CDC • ODS • DATA MART • Big Data • Hadoop
- 11. Fashion Terms (optional) • OLTP / OLAP • ELT • CDC • ODS • DATA MART • Big Data • Hadoop
- 12. Q & A
Notes de l'éditeur
- Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.
- Normalized: 3NF model,Dimensional Model: star schema.Sample:Build a query to retrieve the transaction data with a particular product type (Hard disk) and particular customer type (Retailer)SampleProducts, Product Type, Customer, Customer Type, Salestransaction
- Why not query data directly?OLTP influenceDifferent model thinkingNot all source data are useful, integration purpose.(Option)SCD1,2,3Surrogate key8 hrs time window
- Report productIBMcognosMicrosoft ssrs
- OLTP:Online transaction processing OLAP:Online analysis processingELT: Extract Load TransformCDC: Changed Data CaptureODS: Operational data storeData Mart: sub-set (subject aspect) modelBig Data:Hadoop:
- OLTP:Online transaction processing OLAP:Online analysis processingELT: Extract Load TransformCDC: Changed Data CaptureODS: Operational data storeData Mart: sub-set (subject aspect) modelBig Data:Hadoop: