Data Warehousing Lecture-4 Introduction and Background
How is it Different? • Starts with a 6x12 availability requirement ... but 7x24 usually becomes the goal. • Decision makers typically don’t work 24 hrs a day and 7 days a week. An ATM system does. • Once decision makers start using the DWH, and start reaping the benefits, they start liking it… • Start using the DWH more often, till want it available 100% of the time.
How is it Different? • Starts with a 6x12 availability requirement ... but 7x24 usually becomes the goal. • For business across the globe, 50% of the world may be sleeping at any one time, but the businesses are up 100% of the time. • 100% availability not a trivial task, need to take into account loading strategies, refresh rates etc.
Requirements Program How is it Different? • Does not follows the traditional development model • Classical SDLC • Requirements gathering • Analysis • Design • Programming • Testing • Integration • Implementation
DWH Program Requirements How is it Different? • Does not follows the traditional development model • DWH SDLC (CLDS) • Implement warehouse • Integrate data • Test for biasness • Program w.r.t data • Design DSS system • Analyze results • Understand requirement
Data Warehouse Vs. OLTP OLTP (On Line Transaction Processing) Select tx_date, balance from tx_table Where account_ID = 23876;
Data Warehouse Vs. OLTP DWH Select balance, age, sal, gender from customer_table, tx_table Where age between (30 and 40) and Education = ‘graduate’ and CustID.customer_table = Customer_ID.tx_table;
Data Warehouse Vs. OLTP OLTP: OnLine Transaction Processing (MIS or Database System)
Comparison of Response Times • On-line analytical processing (OLAP) queries must be executed in a small number of seconds. • Often requires denormalizationand/or sampling. • Complex query scripts and large list selections can generally be executed in a small number of minutes. • Sophisticated clustering algorithms (e.g., data mining) can generally be executed in a small number of hours (even for hundreds of thousands of customers).
Putting the pieces together www data OLAP Servers (Tier 2) Clients (Tier 3) Semistructured Sources Query/Reporting MOLAP Extract Transform Load (ETL) Analysis Business Users ROLAP IT Users Data Mining Operational Data Bases Business Users Archived data Data (Tier 0) Data Warehouse Server (Tier 1) Meta Data Data Warehouse Data sources Data Marts Tools